Int. J. Bioinformatics Research and Applications, Vol. x, No. x, xxxx
1
Copyright ? 200x Inderscience Enterprises Ltd. A study of the repetitive structure and distribution of short motifs in human genomic sequences Abanish Singh Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019, Arlington, USA E-mail: singh@https://www.doczj.com/doc/0317172618.html, Cedric Feschotte* Department of Biology, University of Texas at Arlington, TX 76019, Arlington, USA E-mail: cedric@https://www.doczj.com/doc/0317172618.html, *Corresponding author Nikola Stojanovic Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019, Arlington, USA E-mail: nick@https://www.doczj.com/doc/0317172618.html, Abstract: Over the last several years the search for functional elements in human and other genomes by exploiting motif over-representation became increasingly popular. However, about half of the human genome consists of known repeated elements, and that is also the case with most higher eukaryotes. In this study we have shown that in addition to these known repeats, human genomic sequences feature many short motifs which are significantly over-represented, and that their frequency varies only slightly between random repeat-masked sequences and regions located immediately upstream of the known genes. As the ongoing ENCODE project is set on the development of the techniques for high-throughput identification of the functional elements in the human genome, concentrating on about 1% of its entire DNA sequence,
we have chosen these regions for a part of our study. Keywords: DNA; repeated sequences; functional elements; sequence motifs. Reference to this paper should be made as follows: Singh, A., Feschotte, C. and Stojanovic, N. (xxxx) ‘A study of the repetitive structure and distribution of short motifs in human genomic sequences’, Int. J. Bioinformatics Research and Applications , Vol. x, No. x, pp.xxx–xxx. Biographical notes: Abanish Singh received his ME Degree in Computer Science and Engineering in 2000 from the Motilal Nehru National Institute of Technology, Allahabad, India. Before joining the Doctoral Program at the University of Texas at Arlington in 2004, he served on the faculty of the Department of Computer Science and Engineering at the Sant Longowal
2 A. Singh et al.
Institute of Engineering and Technology, Longowal, India, since 1993.
His current PhD Thesis work is in bioinformatics and computational biology,
specifically in the genomic sequence analysis and motif discovery.
Cedric Feschotte received his PhD Degree in Biology in 2001 from the
University of Paris VI, France. He was a postdoctoral research associate at the
Departments of Plant Biology and Genetics at the University of Georgia from
2001 to 2004, where he worked with Dr. Susan Wessler on transposable
elements in plant genomes. Since 2004, he is an Assistant Professor in the
Department of Biology at the University of Texas at Arlington. His current
research focus is on the evolutionary history and genomic impact of mobile
genetic elements, with an emphasis on the human genome.
Nikola Stojanovic received his PhD Degree in Computer Science and
Engineering in 1997 from the Pennsylvania State University, University Park,
PA. After five years of working on the Human Genome Project at the
Whitehead Institute/MIT Center for Genome Research in Cambridge, MA,
he joined the faculty of the University of Texas at Arlington in 2003, as an
Assistant Professor. His research interests are in algorithms for genomic
sequence analysis, phylogenetic studies and sequence alignments.
1Introduction
The search for transcription factor binding sites is one of the most popular sub-fields of bioinformatics, and many algorithms have been developed over more than a decade of intensive research. The first approaches relied on a rather naive assumption that the target sites for protein binding must feature information content sufficient for them to be recognised. Disillusionment soon followed, as any attempt to isolate functional elements in DNA resulted in an enormous number of false positives. Recent approaches have thus concentrated on the incorporation of additional information to the raw sequence data. They often relied on the phylogenetic conservation (Stojanovic et al., 1999; Jegga et al., 2002; Sharan et al., 2003; Corcoran et al., 2005) or a search for clusters of elements whose sequences match experimentally confirmed consensus motifs (Jegga et al., 2002; Sharan et al., 2003) retrieved from databases such as TRANSFAC (Matys et al., 2006) or Jaspar (Sandelin et al., 2004). The latter methods exploited the fact that proteins involved in the initiation of transcription rarely, if ever, act in isolation.
With the advances in microarray technology large sets of putatively co-expressed genes became available. This, in turn, stimulated the development of new techniques aiming to detect conserved motifs in the upstream sequences of these genes (Hughes et al., 2000; Jegga et al., 2002; Bannai et al., 2004). It is intuitive that if a group of genes is coordinately regulated, it should be controlled by the same transcription factors. From the hypothesis that protein binding is directed by DNA sequence motifs it follows that same motifs should be present in all observed upstream regulatory sequences, moreover as a cluster, or multiple clusters representing targets for the transcriptional initiation complexes (Jegga et al., 2002; Johansson et al., 2003; Sharan et al., 2003). This led to the exploitation of motif over-representation in the target regions. In addition, it has been observed in yeast that the promoter regions are often characterised by multiple occurrences of the same binding motif (van Helden et al.,
A study of the repetitive structure and distribution of short motifs 3 1998), and it has been postulated that it may also be the case in higher eukaryotes. Along with the expectation of a co-occurrence of motifs in different regulatory sequences, this postulate stimulated the search for over-represented, or ‘surprise’, motifs (Apostolico et al., 2000; van Helden, 2004).
As the search for non-coding functional elements in newly sequenced genomes intensified, a comprehensive study of the effectiveness of many motif-finding tools has been performed (Tompa et al., 2005). Not surprisingly, it has shown that, while there has been some success in the binding site recognition, the existing methods are not nearly satisfactory. There are several reasons for that. Spatial configuration of DNA, along with other epigenetic phenomena, may be a major factor in transcription factor binding, and no currently available tool incorporates this information. Transcrip-tion factors generally feature non-specific binding preferences (Balhoff and Wray, 2005), and that permits variations in the motif consensus. To make things worse, transcription factor binding sites are short, and their detection may be hampered by pieces of repeated or randomly conserved short sequences. Our own previous study has shown that it is indeed the case (Stojanovic et al., 1999). This, in turn, may force us to comparatively look at homologous or paralogous sequences which have diverged so much that the proteins that bind there are only similar, but no longer same. The solution to this problem may lie in the simultaneous study of a large number of closely related sequences, which are becoming increasingly available. This was one of the major methods of the ENCODE project (The ENCODE Project Consortium, 2004), which in its pilot phase aimed at the development of high-throughput techniques for the classification of DNA elements within a set of target regions comprising approximately 1% of the human genome.
It is well known that even non-functional parts of a genome are not a random assembly of four letters. The analysis of statistical features of sequences often involves a non-trivial background model, such as these based on Markov Models, or Hidden Markov Models. In this study we have done extensive simulations and analysis of real data in order to identify short motif conservation patterns in the human genome, and in particular in the ENCODE target regions. While the number of repeated elements in randomly generated synthetic sequences was almost perfectly conforming to the Poisson expectation, the number of repeated substrings in repeat-masked random intergenic sequences was far greater than expected. This bias appears to be genome-wide, as it persisted even when we simultaneously considered many additional randomly collected human sequences, varying in size between 100 and 4,000 characters. In consequence, any search for conserved motifs is bound to return many results, and, depending on what we search for, most would likely be false positives.
In order to gain a better perspective on our ability to characterise significant over-represented motifs in different regions of the genome, we have looked at the number of short (<20 bp) repeats, both exact and approximate in various genomic environments and synthetic sequences. Although studies have been done regarding the distribution of tandem repeats (Bilgen et al., 2004) and larger interspersed repeats (International Human Genome Sequencing Consortium, 2001), we are unaware of any systematic examination of the genome-wide occurrences of very short interspersed motifs.
4 A. Singh et al.
2 Distribution of short exact repeated motifs
We started the analysis by creating six different data sets, each consisting of 100 sequences of 500 bases in length. Although we looked at other possible segment lengths, as short as 50, and as long as several thousand, the results on short exact sequences were not substantially different and length 500 was well suited for the consideration of regions immediately 5’ to known genes. Although the issue is still unresolved, some studies have shown that most cis-acting regulatory elements appear to cluster in the gene upstream regions of about this length (Khambata-Ford et al., 2003).
Four of our data sets were synthetic, containing sequences created by assembling of A’s, C’s, G’s and T’s using a random number generator on Unix, and sequences generated by second, third and fifth order Markov Models, trained on one million bases taken from human chromosome 2 obtained through the Ensembl (Birney et al., 2006) genome browser. These specific MMs have been selected because the second and the third order are widely used in the simulation of genetic sequences, and the fifth order is popular in gene-finding tools. We were especially interested in the behaviour of the second order Markov Model, as it has been used to generate control sequences in a comprehensive evaluation of motif-finding tools (Tompa et al., 2005). Strings generated by even higher order MMs were considered in order to confirm trends, but not studied in detail. The remaining two sets were real DNA sequences: one was constructed from the upstream regions immediately 5’ to annotated Ensembl human genes and the other consisted of random repeat-masked human intergenic sequences. The total length of sequences in each set was 50,000 bases (300,000 letters total).
In order to count short exact repeated oligonucleotides we used a modification of the Karp–Rabin pattern matching algorithm (Karp and Rabin, 1987), locating all repeats of specified length in time linear with the size of the sequence. The original Karp–Rabin method was based on numerical keys to code patterns, and we used such keys as indices to a hash table counting the number of occurrences of each motif. We ran our program separately for motif lengths varying between 4 and 9, and recorded the total numbers of repeated elements in Table 1. Since the repeats have been counted separately for each of the 100 sequences in each set, the recorded values include the mean (μ)and the standard deviation (σ)for all runs. In addition to the empirically determined counts we have also recorded the expected numbers of the repeats, based on the Poisson model.
As it can be seen from Table 1, there were only insignificant differences between the models for motifs of length 4.1 Starting with length five, an obvious pattern emerges, in which the number of repeats in sequences created by the random number generator correlates with Poisson predictions very well, but none of the other models do. Indeed, a chi-square test on the columns of Table 1 (μ values), whose results are shown in Table 2, confirmed with very high confidence that random synthetic sequence draws from the same distribution as Poisson prediction, but rejected other sets (except, weakly, the higher order MMs). There were more repeats than expected in all Markov Models and they corresponded well to each other, confirmed by solid p-values. A weak similarity has also been found between the second order Markov Model and random intergenic sequences. This, on one hand, justifies its use in modeling genomic environments, but it also advises caution concerning the use of the MMs in simulations.
A study of the repetitive structure and distribution of short motifs 5 Table 1The mean numbers (μ) and standard deviations (σ) of repeated patterns of different lengths in different types of nucleotide sequences. Pattern counting has been done
over 100 sequences of length 500 in each category
Pattern length Expected
number
Random
synthetic
2nd order
Markov M.
3rd order
Markov M.
5th order
Markov M.
Random
genomic
Upstream
regulatory
4 429.06
μ = 425.74 μ = 437.99 μ = 432.84 μ = 432.23 μ = 438.97 μ = 433.92
σ = 6.36 σ = 8.12 σ = 7.1 σ = 6.91 σ = 8.5 σ = 9.94
5 193.16
μ = 189.18 μ = 237.83 μ = 222.98 μ = 222.27 μ = 261.64 μ = 260.11
σ = 15.59 σ = 17.0 σ = 16.68 σ = 15.83 σ = 33.49 σ = 30.67
6 57.46
μ =55.16 μ = 84.33 μ = 74.58 μ = 75.88 μ = 106.62 μ = 115.31
σ = 12.51 σ = 15.16 σ = 13.27 σ = 14.66 σ= 43.5 σ = 37.72
7 15.03
μ = 14.0 μ = 24.5 μ= 21.82 μ = 23.3 μ = 38.66 μ= 47.54
σ = 5.77 σ = 9.97 σ = 7.81 σ = 9.48 σ = 44.31 σ= 29.88
8 3.8
μ = 3.12 μ = 7.05 μ = 5.75 μ= 6.87 μ= 15.72 μ = 21.3
σ = 2.75 σ = 5.15 σ = 4.16 σ = 5.19 σ= 44.26 σ = 21.62
9 0.95
μ = 0.56 μ = 1.94 μ = 1.47 μ= 1.97 μ= 8.57 μ= 11.33
σ = 1.17 σ = 2.42 σ = 1.92 σ = 2.25 σ = 44.04 σ = 15.67
Table 2 Chi-square confidence levels for the compared data sets, indicating the likelihood that sequences in the compared set pairs (column-wise in Table 1) have indeed been drawn
from the same distribution. MMn abbreviates nth order Markov model
Expected number Random
synthetic
2nd order
Markov M.
3rd order
Markov M.
5th order
Markov M.
Random
genomic
Upstream
regulatory
Expected 1.0 >0.995 <0.005 ≈0.02 <0.005 <<0.005 <<0.005 Random >0.995 1.0 <0.01 ≈0.2 ≈0.1 <<0.005 <<0.005 MM2 <0.005 <0.01 1.0 ≈0.6 ≈0.8 ≈0.025 <0.005
MM3 ≈≈0.2 ≈0.6 1.0 >0.995 <0.005
<0.005 MM5 < ≈0.1 ≈0.8 >0.995 1.0 <0.005
<0.005 Genomic << <<0.005 ≈0.025 <0.005 <0.005 1.0 ≈0.8 Regulatory << <<0.005 <0.005 <0.005 <0.005 ≈0.8 1.0
It was surprising, and somewhat discouraging for the attempts of locating functional elements through over-representation, that random intergenic repeat-masked (and thus, presumably, reasonably unique) sequences featured about the same number of short repeated motifs as sequences taken upstream of the genes. The chi-square test was conclusive on this, with the p-value indicating a strong agreement. As for the correspondence between the real sequences and the models, random genomic sequences appear to have somewhat similar number of repeats as the second order Markov Model, but are otherwise quite distinct from any other simulated data set. Overall, real genomic sequences were similar to each other, but not to the models, Markov Models mutually agreed well, but otherwise did not show significant similarity to other data sets, and synthetic sequences corresponded well to the Poisson prediction (a ‘sanity check’), but their composition was different than that of Markov Model simulated data, and dramatically different than that of the real sequences.
We next analysed these relationships at a finer granularity, looking separately at each motif length, and for each length separately at the number of motifs repeating n times, where n was varied between two and ten or more (the latter counted together).
6 A. Singh et al.
Although we did full analysis for all motif lengths in our range, the results were similar, and we show the representative sample for motif lengths 4, 7 and 9 in Table 3. As before, the sequences generated by using random numbers corresponded to Poisson predictions consistently well, while there was a discrepancy between these two and all other models. There was a somewhat weak mutual agreement between different Markov Models (at least two of the three corresponded to each other in every test), and occasionally between random genomic and gene upstream sequences, but the fit between the synthetic and real data was consistently poor.
In this round of testing we have applied the chi-square test on all combinations of models, separately for each motif length, using the sums of the number of repeats in 100 runs as our samples x i, where i corresponded to the number of times the motifs have been repeated (so, for instance, in the test for motifs of length 6, x3was the count of motifs of length 6 repeated three times). This method provided us the sufficient sample size in each category i, which could have otherwise been a problem, having in mind the relative scarcity of long exact motifs repeated many times. Unfortunately, for all comparisons except these involving Poisson expectations we needed to estimate the expected values from the data, and thus substantially reduce the number of degrees of freedom, which has made our analysis of longer repeats somewhat unreliable.
Table 3 The mean numbers (μ) and standard deviations (σ)of repeated patterns of length 4, 7 and 9 in different types of nucleotide sequences. For each motif length, the
corresponding rows represent the numbers of motifs repeated n times, where n varies
between two and ten or more. Pattern counting has been done over 100 sequences
of length 500 in each category
Length/ repeats Expected
number
Random
synthetic
2nd order
Markov M.
3rd order
Markov M.
5th order
Markov M.
Random
genomic
Upstream
regulatory
4/2 69.25
μ= 70.28 μ= 50.05 μ= 55.42 μ= 55.16 μ= 45.17 μ= 47.19
σ = 6.2σ = 6.97σ = 6.77σ = 7.94σ = 8.38σ = 9.42 4/3 45.09
μ= 44.86 μ= 37.64 μ= 39.04 μ= 38.93 μ= 31.68 μ= 31.05
σ = 5.84 σ = 5.53 σ = 5.32 σ = 4.99 σ = 8.1 σ = 7.69 4/4 22.02
μ= 21.36 μ= 24.39 μ= 22.7 μ= 23.21 μ= 20.13 μ= 19.0
σ = 3.9 σ = 4.34 σ = 4.7 σ = 4.37 σ = 4.73 σ = 5.27 4/5 8.6
μ= 8.17 μ= 12.8 μ= 11.69 μ= 11.12 μ= 11.65 μ= 11.0
σ = 2.8 σ = 3.18 σ = 3.14 σ = 3.08 σ = 3.12 σ = 3.46 4/6 2.8
μ= 2.83 μ= 5.44 μ= 4.52 μ= 4.56 μ= 6.41 μ= 5.81
σ = 1.73 σ = 2.34 σ = 1.83 σ = 1.86 σ = 2.38 σ = 2.28 4/7 0.78
μ= 0.73 μ= 2.51 μ= 2.16 μ= 2.09 μ= 3.67 μ= 3.17
σ = 0.8 σ = 1.47 σ = 1.38 σ = 1.39 σ = 2.02 σ = 1.9 4/8 0.19
μ= 0.23 μ= 0.94 μ= 0.69 μ= 0.77 μ= 1.8 μ= 1.79
σ = 0.47 σ = 1.01 σ = 0.81 σ = 1.0 σ = 1.39 σ = 1.37 4/9 0.04
μ= 0.03 μ= 0.38 μ= 0.33 μ= 0.41 μ= 1.32 μ= 1.13
σ = 0.17 σ = 0.64 σ = 0.57 σ = 0.62 σ = 1.43 σ = 1.35 4/10+ 0.01
μ= 0 μ= 0.16 μ= 0.12 μ= 0.23 μ= 0.79 μ= 0.74
σ = 0 σ = 0.39 σ = 0.32 σ = 0.51 σ = 1.56 σ = 1.09 7/2 7.4
μ= 6.97 μ= 11.8 μ= 10.39 μ= 10.86 μ= 15.85 μ= 18.51
σ = 2.87σ = 4.85σ = 3.64σ = 4.12σ = 6.58σ = 9.25 7/3 0.07
μ= 0.02 μ= 0.3 μ= 0.3 μ= 0.39 μ= 0.75 μ= 1.52
σ = 0.14 σ = 0.61 σ = 0.59 σ = 0.72 σ = 1.62 σ = 2.7
A study of the repetitive structure and distribution of short motifs 7 Table 3 The mean numbers (μ) and standard deviations (σ)of repeated patterns of length 4, 7 and 9 in different types of nucleotide sequences. For each motif length, the
corresponding rows represent the numbers of motifs repeated n times, where n varies
between two and ten or more. Pattern counting has been done over 100 sequences
of length 500 in each category (continued)
Length/ repeats Expected
number
Random
synthetic
2nd order
Markov M.
3rd order
Markov M.
5th order
Markov M.
Random
genomic
Upstream
regulatory
7/4 0.001
μ= 0 μ= 0 μ= 0.01 μ= 0.05 μ= 0.06 μ= 0.47
σ = 0 σ = 0 σ = 0.01 σ = 0.22 σ = 0.24 σ = 1.11
7/5 0
μ= 0 μ= 0 μ= 0.02 μ= 0.03 μ= 0.13 μ= 0.18
σ = 0 σ = 0 σ = 0.14 σ = 0.17 σ = 0.91 σ = 0.52
7/6 0
μ= 0 μ= 0 μ= 0 μ= 0.01 μ= 0.13 μ= 0.13
σ = 0 σ = 0 σ = 0 σ = 0.01 σ = 1.01 σ = 0.44
7/7 0
μ= 0 μ= 0 μ= 0 μ= 0 μ= 0.06 μ= 0.03
σ = 0 σ = 0 σ = 0 σ = 0 σ = 0.42 σ = 0.17
7/8 0
μ= 0 μ= 0 μ= 0 μ= 0 μ= 0.07 μ= 0.06
σ = 0 σ = 0 σ = 0 σ = 0 σ = 0.6 σ = 0.24
7/9 0
μ= 0 μ= 0 μ= 0 μ= 0 μ= 0.14 μ= 0.03
σ = 0 σ = 0 σ = 0 σ = 0 σ = 1.3 σ = 0.17
7/10+ 0
μ= 0 μ= 0 μ= 0 μ= 0 μ= 0.08 μ= 0.03
σ = 0 σ = 0 σ = 0 σ = 0 σ = 0.8 σ = 0.17
9/2 0.48
μ= 0.28 μ= 0.97 μ= 0.72 μ= 0.92 μ= 2.07 μ= 3.81
σ = 0.58σ = 1.21σ = 0.96σ = 1.07σ = 3.18σ = 5.72
9/3 0
μ= 0 μ= 0 μ= 0.01 μ= 0.03 μ= 0.21 μ= 0.33
σ = 0 σ = 0 σ = 0.01 σ = 0.17 σ = 1.6 σ = 0.9
9/4 0
μ= 0 μ= 0 μ= 0 μ= 0.01 μ= 0.05 μ= 0.15
σ = 0 σ = 0 σ = 0 σ = 0.01 σ = 0.26 σ = 0.57
9/5 0
μ= 0 μ= 0 μ= 0 μ= 0 μ= 0.13 μ= 0.07
σ = 0 σ = 0 σ = 0 σ = 0 σ = 1.01 σ = 0.35
9/6 0
μ= 0 μ= 0 μ= 0 μ= 0 μ= 0.13 μ= 0.06
σ = 0 σ = 0 σ = 0 σ = 0 σ = 1.29 σ = 0.28
9/7 0
μ= 0 μ= 0 μ= 0 μ= 0 μ= 0.09 μ= 0.07
σ = 0 σ = 0 σ = 0 σ = 0 σ = 0.8 σ = 0.29
9/8 0
μ= 0 μ= 0 μ= 0 μ= 0 μ= 0.03 μ= 0.01
σ = 0 σ = 0 σ = 0 σ = 0 σ = 0.3 σ = 0.01
9/9 0
μ= 0 μ= 0 μ= 0 μ= 0 μ= 0.01 μ= 0.01
σ = 0 σ = 0 σ = 0 σ = 0 σ = 0.01 σ = 0.1
9/10+ 0
μ= 0 μ= 0 μ= 0 μ= 0 μ= 0.04 μ= 0.01
σ = 0 σ = 0 σ = 0 σ = 0 σ = 0.4 σ = 0.01
Interestingly, while there was a good agreement in the repeat distribution in random genomic and gene upstream sequences for motifs of length 4, the chi-square test failed for every other length. As it can be seen from Table 3 for lengths 7 and 9, gene upstream
8 A. Singh et al.
sequences appear to feature a preference to an increased number of moderately repeated motifs, while random genomic sequences are biased towards smaller numbers of these repeated more dramatically (five or more times). This pattern was consistent for all considered motif lengths, however the fewer motifs of higher repeat count compensated for the lower number of moderately repeated motifs, resulting in an overall similarity in the overall number of repeated sequences throughout our test sets, regardless of their proximity to the genes. Under any circumstances, the number of short repeated motifs in the genomic sequences was greater than in any of the synthetic models, and far greater (an order of magnitude for longer motifs) than the Poisson expectations.
3 Analysis of most common short degenerate motifs
After experiencing this dramatic micro-repetitive structure of the human genome we were interested to find the most common short motifs significantly repeated in the ENCODE regions. Although the program used above was capable of locating short exact motifs in sequences of any length, in linear time, concentrating on perfect conservation appeared to be too restrictive. We have thus used our tool for finding short variable repeated motifs, described in detail in Singh and Stojanovic (2006).
Briefly, our software starts by locating all exact repeated patterns in given sequences, including dinucleotides. This seeding step is done in time slightly super-linear to the length of the sequences, using the suffix tree data structure (Weiner, 1973), which has recently been applied to problems similar to ours (Adebiyi et al., 2001; Bannai et al., 2004). After building the original list of repeats, we use an indexing scheme to quickly locate all neighbours of a given (seed) motif, and search for all pairs that appear to be substantially repeated together, at a fixed distance. Whenever such pairs are found we build the tentative consensus of the approximate motif, and recursively try to extend it with additional seed elements and overlaps. The consensus building continues until a certain quality threshold, usually set to 0.9 (90%) or 0.95, can not be maintained any longer. We label all positions of absolute conservation with uppercase letters, and assign them the weight based on the number of sites participating in the construction of the consensus. Positions featuring a majority character, but occasionally broken with a mismatch are labelled with a lowercase character, whose weight is determined based on the number of sites which agree. When there is no agreement at a position it is signified by character ‘N’. The final consensus motif is reported based on the probabilistic evaluation of its length, weight and the number of occurrences.
Since the program assumes that it has been given a set of sequences, rather than a single one, it considerably reduces the search space by filtering out the motifs which do not appear in the minimal number of distinct segments (a settable parameter). Unfortunately, when the sequences in the input are very similar (such as vertebrate ultra-conserved sequences, or even just homologous sequences from closely related species), this causes a theoretically exponential explosion of the recursive refinement step. However, such situations are rare (after repeat masking, most intergenic sequences do not exhibit good conservation of motifs longer than about a dozen bases), and easily detectable. On average, our software is capable of locating all significantly repeated variable motifs quickly and accurately.
We ran this motif-detection program on the entire set of ENCODE regions, obtained through Ensembl, after masking the repeats. The repeat masking step was done since
A study of the repetitive structure and distribution of short motifs 9 there was little purpose in trying to find common short repeated motifs in the presence of known long repeat elements. We performed 150,000 runs on 5–10 randomly chosen segments of length 1000, setting the program parameters so that only elements, which have been found in all segments were reported. We have not excluded known exons from our test data – it simplified the selection and, since exons generally comprise less than 2% of the human genome we believed that they would not significantly affect our results.
Although we recorded only motifs of length 7 and above, their number was in thousands even after filtering these which were nearly identical or inverse complements of each other, and these featuring extremely simple sequence (all A’s, for instance) or tandem repeats. All these motifs do deserve further classification, but at this time we have limited our study only to about two dozen, which were statistically least likely to occur by chance. Since our repeat masked ENCODE sequences contained 40,645,510 bases, counting both strands, in a completely random string of this length a motif of size 10, for instance, would be expected to be found about 39 times. We used such considerations as a basis for the selection of the top choices, where the effective length of the string was calculated by assigning different weights to differently conserved positions (1 for an uppercase letter, 0.5 for lowercase and 0 for an ‘N’ – this could have been further refined by using the exact weights of the identified motifs, but for this study that was not necessary).
In order to do a tentative characterisation of the discovered motifs, we have checked them against the human entries in RepBase (Jurka, 2000) for possible membership in a known repeat family, and TRANSFAC (Matys et al., 2006) for a possible functional role. Table 4 summarises this information for four longest motifs in our list (the fifth one of that size was (CTG)4, which we filtered out), and Table 5 provides the same account for the top five motifs after these with two or less G’s or C’s were removed. The remaining top motifs were either degenerated poly-A’s (or poly-T’s), or a combination of A’s and T’s. Although they are also potentially significant, we have not studied them in detail, since poly-A tails are known to be present in many copies in genomic sequences. One explanation for their prevalence is in that they are derived from the terminus of non-LTR retrotransposon repeats. These elements are abundant in the human genome (over 2.3 million copies spanning over one third of the genome) and they are characterised by a stretch of poly-A’s at their 3’ end (International Human Genome Sequencing Consortium, 2001). Because of its variable length and rapid mutational degradation, part or all of the 3’ poly-A terminus of non-LTR retrotransposons may often remain after repeat masking.
Even as the number of matches our top motifs had in RepBase generally exceeded what would be expected by chance only, these hits were not concentrated in a single repeat class, and thus probably do not represent remnants of a particular mobile element, at least not one of a known classification. Similarly, their TRANS-FAC matches do not appear to lend strong support to the hypothesis that they may be functional protein binding sites – the examined sequence was human, but most of the hits were in non-human elements, or elements which are common in repeated sequences throughout the mammalian lineage (or even broader). While these motifs are clearly strongly repetitive, and some also likely functional, further studies are needed in order to characterise their nature and origins.
10 A. Singh et al.
Table 4 Longest repeated consensus motifs in the ENCODE regions, after filtering known repeats, simple sequence and tandem repeats. Uppercase letters indicate positions
which were perfectly conserved, lowercase letters represent positions where there
was a clear majority character, and N’s indicate non-conserved positions. Only 100%
TRANSFAC hits are listed. If there was such hit within human factors, others, if any,
were omitted. In the absence of a human hit, other factors are indicated by (*)
following the factor name
Motif consensus Effective
length
Expected
count in
ENCODE
Occurrences
in ENCODE
Matches in
RepBase
Matches in
TRANSFAC
TTTaNaAAAGAAA 11 9.7 135 Multiple
(5) NF-AT1 ATGTNtNTTAAA 9.5 77.5 327 Multiple (5) MIG (*) CTGTTTNaNTTT 9.5 77.5 281 Multiple
(5)
HNF-3α, HNF-3B AAAATgNcTTTT 10 38.8 125 Multiple
(6)
YY1
Table 5 Five most significant repeated consensus motifs in the ENCODE regions, after filtering known repeats, simple sequence, tandem repeats and GC-poor repeats.
Upper case letters indicate positions which were perfectly conserved, lowercase
letters represent positions where there was a clear majority character, and N’s indicate
non-conserved positions. Only 100% TRANSFAC hits are listed. If there was such hit
within human factors, others, if any, were omitted. In the absence of a human hit,
other factors are indicated by (*) following the factor name
Motif consensus Effective
length
Expected
count in
ENCODE
Occurrences
in ENCODE
Matches in
RepBase
Matches in
TRANSFAC
CCCAgNNCTG 7.5 310.1 2692 Multiple (47) SV40 – unknown
(*) GGGNNcTGGG 7.5 310.1 2621 Multiple (37) SV40 – unknown
(*) AGANNcAGAA 7.5 310.1 2586 Multiple
(81) – CTGNNtTCCT 7.5 310.1 2251 Multiple (56) E74A (*) AGGNNtGGGG 7.5 310.1 2240 Multiple
(42) –
4 Discussion
The micro-repetitive structure of the human genome we have described advises us caution when using over-representation for the determination of functional DNA elements. At present, most motif-finding tools do not solely rely on a single motif frequency, taking into account other features of biological relevance, such as clustering of the motifs, matching against experimentally confirmed consensus patterns or evolutionary conservation. While each of these approaches has merits, they all have weaknesses. Clustering of over-represented motifs may very well be a consequence of their common source in an ancient repeat (i.e., transposed sequence) not recognised by the RepeatMasker or other repeat-finding tools, such as Re-peatScout (Price et al., 2005). Almost all most frequent motifs we looked at in ENCODE had hits in RepBase, and often in TRANSFAC, too. On the other hand, methods based on phylogenetic footprints may
A study of the repetitive structure and distribution of short motifs 11 be overly dependent on positional conservation. The ultimate classification of any DNA segment must be done in the laboratory, and the construction of a detailed map of the repetitive landscape of the genome can be of great help in this process.
The fact that for exact motifs of length 5 or more there was no good chi-square agreement between random genomic and gene upstream sequences is potentially significant. The noticed bias towards more copies of single motifs in intergenic regions may indicate the same origin of the sequences, presumably by many overlapping insertions of DNA mobile elements, but different selection pressures after some of the regions acquired a functional role. Under this scenario, further insertions, which would lead to even more random motif copies in non-functional segments would be harmful in regulatory regions, and thus selected against. TRANSFAC hits in organisms other than human would also point in this direction, as parts of ancient repeats shared among the species may have acquired function in some, but not all of them.
Even if database lookups for functional patterns often result in a large number of false positives, the fact that not every one of our top motifs had a match indicates that a selection of sensible candidates for further study is possible. In another project (manuscript in preparation) we have collected seven putative direct targets for the Mixed Lineage Leukemia (MLL) transcriptional complexes (Hess, 2004), and identified 11 significant common motifs, which are currently being investigated in the laboratory. While we expect that some of these, and possibly most, would be noise, we are confident that a few would indeed carry a signal.
Our study can be made more complete in many ways. We need to map the discovered motifs back to their original genomic locations, looking at their groupings and experimental evidence. If most of the short frequent motifs indeed originate in layered ancient transpositional activity, some of them will show significant patterns of co-occurrence. On the other hand, we may have been too far reaching when taking on the analysis of the human genome first. It would be worthwhile to also look at a simpler genome, like that of a worm, and we shall direct some of our efforts in that direction in the future.
Acknowledgements
The authors would like to thank Dr. Subhrangsu Mandal of the UTA Department of Chemistry and Biochemistry for valuable comments on our results and the nature of transcriptional regulation, as well as for the help in classifying potential protein binding motifs. This work has been partially supported by UTA REP grant 14 7487 62 to NS. References
Adebiyi, E.F., Jiang, T. and Kaufmann, M. (2001) ‘An efficient algorithm for finding short approximate non-tandem repeats’, Bioinformatics, Vol. 17, pp.S5–S12.
Apostolico, A., Bock, M.E., Lonardi, S. and Xu, X. (2000) ‘Efficient detection of unusual words’, J. Comput. Biol., Vol. 7, pp.71–94.
Balhoff, J.P. and Wray, G.A. (2005) ‘Evolutionary analysis of the well characterized endo16 promoter reveals substantial variation within functional sites’, PNAS, Vol. 102, pp.8591–8596.
12 A. Singh et al.
Bannai, H., Inenaga, S., Shinohara, A., Takeda, M. and Miyano, S. (2004) ‘Efficiently finding regulatory elements using correlation with gene expression’, J. https://www.doczj.com/doc/0317172618.html,put. Biol., Vol. 2, pp.273–288.
Bilgen, M., Karaca, M., Onus, A.N. and Ince, A.G. (2004) ‘A software program combining sequence motif searches with keywords for finding repeats containing DNA sequences’, Bioinformatics, Vol. 20, pp.3379–3386.
Birney, E., Andrews, D., Caccamo, M. et al. (2006) ‘Ensembl 2006’, Nucleic Acids Res., Vol. 34, pp.D561–D453. AUTHOR PLEASE PROVIDE REMAINING AUTHORS NAME. Corcoran, D.L., Feingold, E., Dominick, J., Wright, M., Harnaha, J., Trucco, M., Giannoukakis, N.
and Benos, P.V. (2005) ‘Footer: a quantitative comparative genomics method for efficient recognition of cis-regulatory elements’, Genome Res., Vol. 15, pp.840–847.
Hess, J.L. (2004) ‘MLL: a histone methyltransferase disrupted in leukemia’, Trends Mol. Med., Vol. 10, pp.500–507.
Hughes, J.D., Estep, P.W., Tavazoie, S. and Church, G.M. (2000) ‘Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae’, J. Mol. Biol., Vol. 296, pp.1205–1214.
International Human Genome Sequencing Consortium (2001) ‘Initial sequencing and analysis of the human genome’, Nature, Vol. 409, pp.860–921.
Jegga, A.G., Sherwood, S.P., Carman, J.W., Pinski, A.T., Phillips, J.L., Pestian, J.P. and Aronow, B.J. (2002) ‘Detection and visualization of compositionally similar cis-regulatory element clusters in orthologous and coordinately controlled genes’, Genome Res., Vol. 12, pp.1408–1417.
Johansson, O., Alkema, W., Wasserman, W.W. and Lagergren, J. (2003) ‘Identification of functional clusters of transcription factor binding motifs in genome sequences: the MSCAN algorithm’, Proceedings of the 11th International Conference on Intelligent Systems in Molecular Biology, pp.169–176.
Jurka, J. (2000) ‘Repbase Update: a database and an electronic journal of repetitive elements’, Trends Genet., Vol. 9, pp.418–420.
Karp, R. and Rabin, M. (1987) ‘Efficient randomized pattern matching algorithms’, IBM J. Res.
Development, Vol. 31, pp.249–260.
Khambata-Ford, S., Liu, Y., Gleason, C., Dickson, M., Altman, R.B., Batzoglou, S. and Myers, R. (2003) ‘Identification of promoter regions in the human genome by using
a retroviral plasmid library-based functional reporter gene assay’, Genome Res., Vol. 13,
pp.1765–1774.
Matys, V., Kel-Margoulis, O.V., Fricke, E. et al. (2006) ‘TRANSFAC? and its module TRANS compel?: transcriptional gene regulation in eukaryotes’, Nucleic Acids Res., Vol. 34, pp.D108–D110. AUTHOR PLEASE PROVIDE REMAINING AUTHORS NAME.
Price, A.L., Jones, N.C. and Pevzner, P.A. (2005) ‘De novo identification of repeat families in large genomes’, Proceedings of the 13th International Conference on Intelligent Systems in Molecular Biology, pp.351–358.
Sandelin, A., Alkema, W., Engstrom, P., Wasserman, W. and Lenhard, B. (2004) ‘JASPAR: an open-access database for eukaryotic transcription factor binding profiles’, Nucleic Acids Res., Vol. 32, pp.D91–D94.
Sharan, R., Ovcharenko, I., Ben-Hur, A. and Karp, R.M. (2003) ‘CREME: a framework for identifying cis-regulatory modules in human-mouse conserved segments’, Proceedings of the 11th International Conference on Intelligent Systems in Molecular Biology, pp.283–291. Singh, A. and Stojanovic, N. (2006) ‘An efficient algorithm for the identification of repetitive variable motifs in the regulatory sequences of co-expressed genes’, Proceedings of the 21st International Symposium on Computer and Information Sciences, Springer-Verlag LNCS, Vol. 4263, pp.182–191.
A study of the repetitive structure and distribution of short motifs 13 Stojanovic, N., Florea, L., Riemer, C., Gumucio, D., Slightom, J., Goodman, M., Miller, W. and Hardison, R. (1999) ‘Comparison of five methods for finding conserved sequences in multiple alignments of gene regulatory regions’, Nucleic Acids Res., Vol. 27, pp.3899–3910.
The ENCODE Project Consortium (2004) ‘The ENCODE (ENCyclopedia Of DNA elements) Project’, Science, Vol. 306, pp.636–640.
Tompa, M., Li, N., Bailey, T.L. et al. (2005) ‘Assessing computational tools for the discovery of transcription factor binding sites’, Nature Biotechnology, Vol. 23, pp.137–144. AUTHOR PLEASE PROVIDE REMAINING AUTHORS NAME.
van Helden, J., Andre, B. and Collado-Vides, J. (1998) ‘Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies’, J. Mol. Biol., Vol. 281, pp.827–842.
van Helden, J. (2004) ‘Metrics for comparing regulatory sequences on the basis of pattern counts’, Bioinformatics, Vol. 20, pp.399–406.
Weiner, P. (1973) ‘Linear pattern matching algorithms’, Proceedings of the 14th IEEE Symposium on Switching and Automata Theory, pp.1–11.
Note
1However, when the individual numbers of motifs occurring a particular number of times were taken into account there were considerable differences between the models, as outlined below.
工作联系函格式范文6篇 Sample format of work contact letter 编订：JinTai College
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高考语文作文备考：名校模拟题作文及例文汇编 四、写作（60分） 10．阅读下面的材料，根据要求写作。 近日，某中学举行了一场以“拥有了实力，行事是该高调还是该低调”为主题的辩论赛。 正方的观点是：拥有了实力，行事就应高调。因为拥有了实力，你也掩盖不了，高调做事是一种责任，一种气魄，一种精益求精的风格，一种执著追求的精神。 反方的观点是：拥有了实力，行事也应低调。因为做事必先做人，低调是一种姿态，一种修养，一种风度，一种智慧，一种谋略。 时代发展到今天，无论个人、集体还是国家，你觉得拥有了实力应该采取何种态度呢？请你在正反双方中选择一方观点作为自己的立场，写一篇辩论稿。要求：选好角度，确定立意，自拟标题，注意格式；不要套作，不得抄袭，不少于800字。 【分析】本题考查学生写作的能力。本文属于任务驱动型文章。写作时要整体理解把握材料的内容，明白材料蕴含的道理，选取合适的角度进行立意构思。写作指令：选择辩题，写一篇辩论稿。写好材料作文的关键在于理解材料主旨并进行立意。本题材料的中心事件是某中学举行的一场以“拥有了实力，行事是该高调还是该低调”为主题的辩论赛，正方的观点是拥有了实力，行事就应高调，反方的观点是拥有了实力，行事也应低调。考生可以选择正方观点，指出高调做事是一种责任，一种气魄，一种精益求精的风格，一种执著追求的精神；可以选择反方观点，指出做事必先做人，低调是一种姿态，一种修养，一种风度，一种智慧，一种谋略。最后还要分析文体，材料要求写成“辩论稿”，这就要求观点要明确，辩论要有力，论据要充足。无论选择哪一个辩题，分析都要有思辩性，切不可完全否定对方的观点（将对方说得一无是处）。 参考立意：1、高调做事是一种责任，一种气魄。2、低调做人是一种修养，一种风度。 【解答】低调做人是一种智慧 尊敬的各位评委老师、同学们： 大家好，我方的辩题是“低调做人是一种智慧”。 山不解释自己的高度，并不影响它的耸立云端；海不解释自己的深度，并不影响它容纳百川；地不解释自己的厚度，但没有谁能取代她作为万物的地位……做人，我认为关键是要学会低调，低调是一种人生的智慧。 莫言是中国第一个获得诺贝尔奖的人，圆了所有中国人几个世纪的“诺奖梦”。但让人可歌
2018高考任务驱动型作文习题及范文汇编 阅读下面的材料，根据要求写一篇不少于800字的文章。（60分）【原创预测题8】《南方周末》报道，2016年4月14日，在山东发生了一起辱母杀人案。女企业家苏银 霞欠了当地涉黑团伙头目吴学占的高利贷，仍欠17万未还清。被于欢刺死的杜志浩等人有 涉黑行为，辱骂殴打、限制并侮辱于母等情节恶劣，警察调解无效后，于欢拿起办公室桌上水果刀，乱刺催债人员，至一死三伤,, 2017年3月17日山东省淄博城市法院一审以故意伤害罪判处于欢无期徒刑，还需赔偿死者家属30598.5元和伤者53443.47元。在社会引起强烈深刻的反响。案件进展：全国高 检派人、山东高法介入指导工作。2017年6月23日山东高法二审裁定于欢犯故意伤害罪， 判处有期徒刑五年并经济赔偿。 比较情、理和法三者，你认为哪个更重要？请综合材料内容及含意作文。要求选好角度，确定立意，明确文体，自拟标题；不要套作，不得抄袭。 法律，高于情理的利剑 ①2016年，在山东发生一起辱母杀人案。由于嫌疑人于欢杀人合情合理，且受害者为 涉黑人员，引起社会强烈反响。最终涉黑者被抓，于欢二审得到减刑。此案看似于欢为母出气，合情合理但不合法。法律是高悬情与理之上的利剑，它约束情与理，以免失控。 ②从事件本身看，我认为，于欢因母亲受辱，在多方求助无果的情况下挥刀，这个举动 似乎合情合理。哪一个孩子看见母亲受辱能无动于衷？但用刀伤人，属于防卫过当，造成严重后果就违法了，他被判刑五年，这是对冲动的惩罚。正因为于欢所为合情合理，因而才引
发社会强烈反响，减轻刑罚并惩处恶人。但合情合理不能作为评判的唯一标准，法律是不可逾越的红线，它是约束情与理的尺度。情理不能大于法，否则社会就会混乱。 ③若没有法律约束，人便为所欲为。荀子曰“木受绳则直，金就砺则利。”进入依法治国时代，任何事物都必须按章办事，一视同仁。就拿网购说吧，它是一件新鲜事，存在利润 空间。初期因法律不健全，假货次品充斥市场，诚信遭遇冰河，网购受到巨大损失。当相关 法律出台，市场秩序迅速恢复，网络经济成为我国支柱产业。因此，合情合理又守法，就会 得到保护，避免遭受损失。最近出台的“信联”措施就很好，严惩那些不讲诚信的违法者。 ④合情合理是谁都想要的但必须依法办事，以其道得之，最终不受处罚。其实，想要减少情与理的影响，依法办事也容易。首先，应明法懂法守法。懂法的途径是读法律条文；其 次，遇事时要冷静，不要完全被情理支配。当然了，法律建立在情理之上，在法律范围内， 保护合情合理的事情。本次事件，于欢正被情理冲昏头脑忘却法律，最终导致悲剧发生。 ⑤因此，我认为在处理看似合情合理的事物前，要理性与理智的思考，自己行为的利弊得失，然后在行动。最后，请时刻谨记康德的话：世界上有两样东西是我最敬畏的，一是天 上的星星，另一个是道德法律。知法明理，敬法守法。只有如此，才不会完全被情理支配， 依法办事，就不会出现问题。 ⑥然而，守法并不意味忘却情理。在现实社会中，经常出现“扶不扶”“救不救”的两难事情。试问那些不扶的人违法吗？没有。他们“不为”却践踏道德底线，伤害情理。那么 世界就会变得冷漠。好在新的法律保护讲道德见义勇者，他们要受到奖励和表彰。 ⑦法律是高悬于情理之上的利剑，遵法守纪是情理的红线。铭记教训，莫让悲剧重演。 辽宁省沈阳市第11中学2018届17班李浩然指导教师：孙延堂
VAR2:=REF(LOW,1); VAR3:=SMA(ABS(LOW-VAR2),3,1)/SMA(MAX(LOW-VAR2,0),3,1)*100; VAR4:=EMA(VAR3*10,3); VAR5:=LLV(LOW,13); VAR6:=HHV(VAR4,13); CURRBARSCOUNT:=DATACOUNT-BARPOS+1; VAR7:=IF(CURRBARSCOUNT>34,1,0); VAR8:=EMA(IF(LOW<=VAR5,(VAR4+VAR6*2)/2,0),3)/618*VAR7; VAR9:=IF(VAR8>100,100,VAR8); 吸筹:VAR9,LINETHICK1,COLOREE00EE; STICKLINE1(VAR9>-120,0,VAR9,3,1); RSV:=(CLOSE-LLV(LOW,9))/(HHV(HIGH,9)-LLV(LOW,9))*100; K:=SMA(RSV,3,1); D:=SMA(K,3,1); J:=3*K-2*D; STICKLINE1(VAR9>1 AND J>REF(J,1) AND REF(J,1)
工作函格式范文 工作函格式范文1 XXX单位： 电子政务外网架设在贵单位机房内，现XXXXX管理信息化平台系统要在电子政务外网上运行，根据实地勘测贵单位机房条件符合平台系统运行条件。 XXXXX管理信息化平台(硬件)承建单位(XXXXX有限责任公司，<工程师3-5名，车辆：>)进入贵单位机房实施平台架设，请贵单位予以支持配合，建设单位进驻贵单位实施此项目需要与贵单位管理的电子政务外网对接，内部网络对接、设备安全、日常维护及设备存放(附件1)由贵单位负责。为了此项目能顺利实施望贵单位能大力支持，我们深表感谢! XXXXX单位(盖章) 20xx年月日 工作函格式范文2 _______公司： 截至_____年__月31日，我公司帐面尚有贵公司欠款_____元(大写人民币_____元整)。按照与贵公司的有关合同协议的约定，贵公司应当在_____年__月__日之前支付上述款项，但我公司至今仍未受到该笔款项。因此，特请贵公司能够在近期内及时向我公司支付上述款项。
此致 _____公司(印章) _____年___月___日 工作函格式范文3 XXXX技术开发总公司： 我司于X年X月X日去函给贵公司，联系我司在XX向贵公司购买二宗用地(7000平方米和3000平方米)的办证事宜，贵公司也根据当时实际情况向我司作出了回复。 近来，上级管理部门在检查我司工作时多次墩促我司着力解决上诉二宗用地的历史遗留问题，明晰权属，为企业改制创造必要条件。为此，我司特致函贵司，望尽快办出二宗用地国土规划两证或复函说明二宗用地的现状，提出比较具体解决问题的办法。 此函 盼复 xxxx年X月X日
中考作文范文汇编10篇 一、作文写作 1．阅读下面的文字，按要求完成微作文。 学校准备为高一新生设计制作校服。原定为一款参照国外校服样式设计的校服，男式帅气，女式俏丽，大家很喜欢。但有家长提出，这样的校服穿上后容易使学生分心，甚至会助长男女同学之间的爱慕之心。校方对此很为难。 请你给校长写一封短信，陈述自己的看法，对解决这一问题提供帮助。要求表达得体，150字左右。 【答案】示例：尊敬的校长： 关于高一新生的校服，我们希望学校能采用原定的设计方案。因为这款校服帅气美丽，能尽显青春气息，展示我们的阳光与朝气。至于有些顾虑，我们可以理解，但并不赞同。因为校服是每个人都穿，大家都美，天天都穿，很是平常。如果某一事物完全融入到我们的生活之中时，我们就不会因此而生出什么特别的关注，只有生活会因此多出一份美丽。我们期待着这份美丽。 祝您工作顺利，心情愉快！ 高一学生：×××【解析】 这是一篇小作文。首先要仔细分析给出的材料，了解到学校定制的这套参照国外校服样式设计的校服深受学生的喜爱，可是学校方面有顾虑。给校长的信要申明学生的观点，观点要具体明确，符合题意，见解深刻，言之有理，有助于解决问题。注意字数的要求。 点睛：这是一篇微作文。“微写作”发源于“微博”“微信”，一般是指根据一定的情景或要求，写作200字左右的“微作文”。“微写作”对考生的写作能力和综合素养提出了更高的要求，中考“微写作”要求考生文字的表达、意境的创造等诸多方面，均应超过网络“微写作”。完成这篇微作文，要明确情境的内容，选择合适的角度写作。 2．作文。 “一切都将会过去；而那过去了的，就会成为亲切的怀恋。”这是普希金《假如生活欺骗了你》中的诗句。诗人告诉我们：生活不可能一帆风顺，当越过坎坷，蓦然回首时，你往往会发现，那些过去了的都已成为人生宝贵的财富，成为你“亲切的怀恋”。同学们，你也曾有过这样的经历和感受吧？请自拟题目，写一篇文章与大家分享。 要求：(1)题目自拟：(2)紧扣材料主题，内容具体充实：(3)有真情实感：(4)文体不限（诗歌、戏剧除外）；(5)不少于600字；(6)文中请回避与你相关的人名、校名、地名。【答案】亲切的怀恋 一切都是瞬息，一切都将会过去；而那过去了的，就会成为亲切的怀恋。 ——题记学习之余，我坐在窗边遐想，想我那遥远的未来、我那充实的现实和那往日的点点滴滴。回忆涌现出的都是那亲切的怀恋。 那是一个阳光明媚的早晨，姥姥教我骑自行车。笨拙的我却驾驭不了这高傲的自行车。我一次次地从自行车上摔落，姥姥一次次将我扶起。她不厌其烦地教我骑自行车的方法，又
期货技术指标详解-随机指数(KDJ) 一、随机指标 随机指标是在威廉指标的基础上，引入移动平均线快、慢的概念，通过计算一定时间内的最高、最低价和收市价间的波幅，反映价格走势的强弱及超买超买动态，分析中、短期股市走势，是比较实用的技术指标。 二、公式 国内计算随机指标的周期为9天，K值D值为3天 RSV(9)=（今日收盘价－9日内最低价）÷（9日内最高价－9日内最低价）×100 K(n)=（当日RSV值+前一日K值）÷N D(n)=（当日K值+前一日D值）÷N J=3K－2D 三、KDJ分析要领 1、 KDJ是中短期技术指标，但需要配合其他技术指标共同分析。 2、 KD线活动范围在1--100之间。D值向上趋近70或超过70时，说明买盘力量大，进入超买区，期市可能下跌。D值向下趋近30 或跌破30时，说明卖方力量很强，进入超卖区，期市的反弹性增强。J值＞100%超买，J值＜10%超卖。 3、当K线与D线交叉时，如果K＞D，说明股市上涨，K线从下方突破D线，行情上涨，可适当买进；如果K＜D，K线从上向下跌破D线，行情转跌，可适当卖出。如果KD线交叉突破反复在50左右震荡，说明行情正在整理，此时要结合J 值，观察KD偏离的动态，再决定投资行为。 4、如果期市层层拔高而KD线层层降低，或完全相反，这种情况称为"价线背离"，预示着股市行情要转向，进入一个多头或空头区位，投资者要及时变换投资行为。股价变动过快时，不适用该指标。 技术指标详解-移动平均线(MA) 移动平均线是应用非常广泛的一种技术指标。它构造简单，客观公正，不易人为操作骗线，受到很多股票投资者的青睐。 一、指数平均数的计算 所谓"移动平均线"是指一段时间内的算术平均线，通常以收盘价作为计算值。其公式如下： MA（N）=第1日收盘价+第2日收盘价+………………+第N日收盘价/N 例如： 把某日的收盘价与前9个交易日的收盘价相加求和，然后再除以10，就可以得到该日的10日移动平均线值MA(10)。 二、移动平均线分类 根据求移动平均线时所用的天数的不同，移动平均线可以产生不同日期的结果。常见的有： a) 短期移动平均线（周线）：即MA（5）以一周的5个交易日为周期的移动平均线
工作联系函的格式及参考范文 工作联系函作为公文中惟一的一种平行文种，其适用的范围相当广泛。对于工作联系函的格式大家清楚么?往下看看，希望对大家有 所帮助! 工作联系函格式 由于函的类别较多，从制作格式到内容表述均有一定灵活机动性。主要介绍规范性公函的结构、内容和写法。 公函由首部、正文和尾部三部分组成。其各部分的格式、内容和写法要求如下： (一)首部。主要包括标题、主送机关两个项目内容。 1、标题。公函的标题一般有两种形式。一种是由发文机关名称、事由和文种构成。另一种是由事由和文种构成。 2、主送机关。即受文并办理来函事项的机关单位，于文首顶格 写明全称或者规范化简称，其后用冒号。 (二)正文。其结构一般由开头、主体、结尾、结语等部分组成。 1、开头。主要说明发函的缘由。一般要求概括交代发函的目的、根据、原因等内容，然后用“现将有关问题说明如下：”或“现将 有关事项函复如下：”等过渡语转入下文。复函的缘由部分，一般 首先引叙来文的标题、发文字号，然后再交代根据，以说明发文的 缘由。 2、主体。这是函的核心内容部分，主要说明致函事项。函的事 项部分内容单一，一函一事，行文要直陈其事。无论是洽工作，询 问和答复问题，还是向有关主管部门请求批准事项等，都要用简洁 得体的语言把需要告诉对方的问题、意见叙写清楚。如果属于复函，还要注意答复事项的针对性和明确性。
(三)结尾。一般用礼貌性语言向对方提出希望。或请对方协助解决某一问题，或请对方及时复函，或请对方提出意见或请主管部门 批准等。 (四)结语。通常应根据函询、函告、函或函复的事项，选择运用不同的结束语。如“特此函询()”、“请即复函”、“特此函告”、“特此函复”等。有的函也可以不用结束语，如属便函，可以像普 通信件一样，使用“此致”、“敬礼”。 (五)结尾落款。一般包括署名和成文时间两项内容。 署名机关单位名称，写明成文时间年、月、日;并加盖公章。 五、撰写函件应注意的问题 函的写作，首先要注意行文简洁明确，用语把握分寸。无论是平行机关或者是不相隶属的行文，都要注意语气平和有礼，不要倚势 压人或强人所难，也不必逢迎恭维、曲意客套。至于复函，则要注 意行文的针对性，答复的明确性。 其次，函也有时效性的问题，特别是复函更应该迅速、及时。像对待其他公文一样，及时处理函件，以保证公务等活动的正常进行。 工作联系函格式范文一 _______公司： 截至_____年__月31日，我公司帐面尚有贵公司欠款_____元(大写人民币_____元整)。按照与贵公司的有关合同协议的约定，贵公 司应当在_____年__月__日之前支付上述款项，但我公司至今仍未受 到该笔款项。因此，特请贵公司能够在近期内及时向我公司支付上 述款项。 此致 _____公司(印章) _____年___月___日 工作联系函格式范文二
期货基础知识及短线KDJ指标详解 期货市场最早萌芽于欧洲。早在古希腊和古罗马时期，就出现过中央交易场所、大宗易货交易，以及带有期货贸易性质的交易活动。最初的期货交易是从现货远期交易发展而来。第一家现代意义的期货交易所1848年成立于美国芝加哥，该所在1865年确立了标准合约的模式。20世纪90年代，我国的现代期货交易所应运而生。我国有上海期货交易所、大连商品交易所、郑州商品交易所和中国金融期货交易所四家期货交易所，其上市期货品种的价格变化对国内外相关行业产生了深远的影响。 最初的现货远期交易是双方口头承诺在某一时间交收一定数量 期货交易走势图的商品，后来随着交易范围的扩大，口头承诺逐渐被买卖契约代替。这种契约行为日益复杂化，需要有中间人担保，以便监督买卖双方按期交货和付款，于是便出现了1571年伦敦开设的世界第一家商品远期合同交易所——皇家交易所。为了适应商品经济的不断发展，改进运输与储存条件，为会员提供信息，1848年，82位商人发起组织了芝加哥期货交易所（CBOT）；1851年芝加哥期货交易所引进远期合同；1865年芝加哥谷物交易所推出了一种被称为“期货合约”的标准化协议，取代原先沿用的远期合同。这种标准化合约，允许合约转手买卖，并逐步完善了保证金制度，于是一种专门买卖标准化合约的期货市场形成了，期货成为投资者的一种投资理财工具。1882年交易所允许以对冲方式免除履约责任，增加了期货交易的流
动性。 如果说短线操作期货，那么我们首选的指标就是KDJ，因为KDJ 指标誉有短线之王的美称。那么我们如何短线运用好KDJ指标呢? 这个是15分钟图，上面直接提示做多做空，我们都知道系统自带的KDJ指标有一定的滞后性，所以在这里研制了智能的KDJ，不光是提示做多做空，还有抄底线和逃顶线，如果到达抄底线那么我们就要及时的做多或者平仓空单，如果到达逃顶线那么我们就要及时的做空或者平仓多单。
公函的格式范文 今天，我给大家介绍的是公函的格式范文，希望对大家有帮助。 1.概述 函,即信函,或称书信。公函,即处理公务所用的书信。公函是党政机关、人民团体、企事业单位间商洽和联系工作时使用的一种文体。 公函的使用范围很广,平行机关或不相隶属机关间联系工作时可以使用公函,上下级机关之间联系、询问、答复工作时,也可以使用公函。 公函根据内容和性质,可以分为很多种类,如:用于商洽的商洽函、用于询问的询问函、用于答复的复函、用于委托的委托函等。另外,还有用于慰问的慰问信、用于祝贺的贺信、公开发表的公开信等,实际上只要它们使用于公务活动中,也应划入广义的公函范围。不过,从狭义的范围来讲,函和信也有一定区别,函的使用比信的范围窄,但比较庄重、严肃。 2.写作要点: 公函在结构上一般由标题、正文、落款三部分构成: (1)标题 跟一般的信函不同,公函的标题通常要包括发文机关、事由和文种类别(函)。有时可省写发文机关,但事由和文种类别不能省略。另外,标题的右上方要有编号。 (2)正文 正文的开头顶格写受文单位,然后另起一段写发函的原因、联系的事项,最后一般要用“请研复”、“请协助”等字样提出具体要求,结束正
文。 (3)落款 法定作者、日期,并加盖公章。 公函的写作要求是:一事一函,语言规范、明了,语气合乎内容要求。 3.范文 范文一 ××市政府关于在铁路压煤改线 ×××站建立交桥的函 [××××]×× ×××委员会: ××铁路压煤改线×××工业站,位于我市×县×××镇。由于该站的建设,原有的×××站西侧的平交道口按设计要求将要封闭。这样就阻断了沟通南北三乡一镇的交通要道,给乡镇企业和商品经济的发展造成了困难。另外,铁路以南五个村的大面积耕地在路北,由于铁路所阻,给群众的生产生活造成了很难克服的困难。工业站在设计在dk308+893处虽有一座净高2.5米,宽6米,长220米的单孔立交兼排水涵洞,但因纵深太长,宽度较窄,高度很低,农机车辆不能通行,农忙秋收人畜难以通过。为此,请贵委员会给予照顾,在dk308+430处(原×××站西侧的平交道口处)建立交桥一座,以适应当地生产和需要。可否盼复。 ××市政府 ××××年××月×日 范文二
八年级作文范文汇编10 篇 一、作文写作 1．根据要求作文。 目： ______如此美 要求：① 在横上添上恰当的，将目充完整；②除歌外，文体不限。③有真 情感，不得套写抄；④用行范的言文字表达；⑤不少于600 字；⑥文章中不 要出真的地名、校名和人名。 【答案】参考例文 微笑如此美 微笑是最好的止痛。人生在世，失在所免，正因有成功的喜悦与失的苦楚，才成了我五 彩的生活。 —— 步入初中以来，人的最大感就是考多，平凡的考，不免有所失意。一眨眼，月考来了，尽 管之前有很多考，但却只是在班上。面一次全校大考，提心吊胆是不必的，然而我却在如 此重要的考中失意了。 那是一个阴森的下午，老在教室里着考的排名，久久未能听我的名字，一个个熟悉的人 名，活像一把把陌生的尖刀，刺痛着我的心。那一卷就像一沉重的石，将我入无底的深 渊。下了，教室里的人影越来越少，最后独留我一个。 一人在与世隔之地，我迷茫的望着窗外，一云遮住了天，太阳同也遮住了我的心。 不知什么候，身后响起了脚步声，越来越清晰，似乎正在向我靠。我不由自主的扭去，是 他，他上着从未停歇的笑容是那么的，以至于我忘了前一秒的 痛。慢慢的，他走到我的身前，并未我感到惊，因他是我最知心的朋友阿A。 很久很久，我都没，只是他始微笑着我。我也不知所措的看着他。目光交的 一刹那，我看到了一种从未有的温暖，加上他那始不的笑容，更像是一熊熊烈火点燃了 我心底最后一希望。 很久之后，他于了：“不要苦着，笑一笑走你的愁吧。”尽管只是一句毫不起眼的，瞬我 体会到了笑的魔力，我的上也开始洋溢微笑，的。 云始未能抵住阳光的威力，我心中的那一片云也消散在微笑之中。微笑是如此的神奇， 又是如此的美，以至于医治好了我心中的痛。 【解析】 【解】 是一道半命作文，作文既了写作情境，又限定了写作范；比全命作文灵活，比作文 更具束性，具有收放自如的特点。半命作文比突出地考了学生的、构思及言表达等多 方面的能力。写作注意以下几点： ①要意，才能确定好的立意。理解“美”的含，“美”，可以从不同角度理解： (1) 具体事物 之美。可以从自己熟悉的人、事、物、景中取一种，而且必是“珍品”来写人、事、物、景的美 或外表、外形的美，如校园如此美，落叶如此美， 某人如此美，家如此美，春天如此美，大海如此美，??(2) 精神面的美：
【篇一：诚信与善良】 ?父亲是一个极其平凡的人。他10岁丧父13岁丧母，14岁独自从老家宁乡动身，一路打零工，历时近一年来到湘北。先是帮人看牛，后作区乡通信员，再到公安派出所当干警，最后调供销社工作。? ?老人又是一个命硬的人。1976年患颈部恶性肿瘤，死里逃生。1978年因血吸虫病引发肝硬化腹水，奇迹般康复。1986年从家里新房子的二楼摔下，至脊椎骶骨骨折。2008年74岁，患带状疱疹等病，以为阎王有请，但最终化险为夷。现身体状况不错，经常骑着一辆除了铃子(没铃)全身都响的旧单车在城里穿行，买菜或者购些日用品、药品等；在家则承担他与母亲老两口的炊事工作。? ?家父不善言辞，但他的三句话，我没齿难忘。? 第一句话，“必须分清公家与私人！”这话说在上世纪70年代末期。那时，我经常到父亲单位上去玩。父亲是多年的商店经理，几乎每天打烊后，都要带领大家一起点数钱币及粮票、布票、油票、糖票、火柴票等有价证券。我总担心钱和有价证券会出问题，谁弄(在我们小镇，“弄”有乘人不备非法谋取的意思)一张到腰包岂不拐了？我好多次都想提醒父亲。有一天与父亲一起回家，我终于把问题提了出来。不想我话一出口，从来性情温和的父亲脸上突变、雷霆大发，全然不顾是在小镇的大街上，“只有你有那样的坏思想！弄钱弄票是犯罪！你要好好地改造自己的思想，以后参加工作必须分清公家与私人，? ?不然以后会成为国家的罪人！”当时不到16岁的我被骂得措手不及、乌龟不眨眼睛，然后是泪眼婆娑。幸亏是在街上，父亲才没动手教育。父亲这么教育我和兄弟姊妹，也是这么做的。父亲工作期间多与钱物打交道，从未出过差错。 第二句话，“老实人不会吃亏的。”1984年起，父亲改任基建仓库保管员。1986年我们家做房子，没占单位半点便宜；借用仓库十多根杉木支撑模板，工程结束及时归还。在将杉木从二楼撤下准备运还仓库时，父亲从楼上摔下。记得有个亲戚从仓库买几包水泥，想多拿几包回去，被父亲严词拒绝。单位上一些人认为我们家建房肯定占了公家的大便宜。仓库就父亲一个人管理，要作弊确实不难。但在父亲因伤病提出提前退休、对仓库进行盘底交接后，一切*大白。仓库账目清楚，实物齐全，升溢16万多元。16万元的升溢，这在当时的一个基层供销社可是天文数字。后来供销社主任亲自上门慰问父亲，说差点冤枉了一个好同志。父亲笑道，“我建房借了一大半。我宁可欠账，也不能拿公家的一包水泥、一根钢筋。一个是图个心安，二个是为崽女带过好头。”主任走后，我小心地问父亲，“您一生清白、老实。您说说，做老实人好吗，划得来吗？”父亲望着已是高中语文老师的儿子，
STICKLINE(C>=O,O,C,8,0),COLORBLACK; STICKLINE(C>=O,H,L,8,0),COLORBLACK; STICKLINE(C<=O,O,C,8,0),COLORBLACK; STICKLINE(C<=O,H,L,8,0),COLORBLACK; A1:=(EMA((OPEN+HIGH+LOW+CLOSE)/4,3)+EMA((OPEN+HIGH+LOW+CLOSE)/4,6)+EMA((OPEN+HI GH+LOW+CLOSE)/4,9))/3; A2:=(EMA((OPEN+HIGH+LOW+CLOSE)/4,5)+EMA((OPEN+HIGH+LOW+CLOSE)/4,10)+EMA((OPEN+ HIGH+LOW+CLOSE)/4,20))/3; A3:=(EMA((OPEN+HIGH+LOW+CLOSE)/4,7)+EMA((OPEN+HIGH+LOW+CLOSE)/4,14)+EMA((OPEN+ HIGH+LOW+CLOSE)/4,28))/3; A4:=(EMA((OPEN+HIGH+LOW+CLOSE)/4,9)+EMA((OPEN+HIGH+LOW+CLOSE)/4,18)+EMA((OPEN+ HIGH+LOW+CLOSE)/4,36))/3; A5:=(EMA((OPEN+HIGH+LOW+CLOSE)/4,11)+EMA((OPEN+HIGH+LOW+CLOSE)/4,22)+EMA((OPEN +HIGH+LOW+CLOSE)/4,44))/3; A6:=(EMA((OPEN+HIGH+LOW+CLOSE)/4,13)+EMA((OPEN+HIGH+LOW+CLOSE)/4,26)+EMA((OPEN +HIGH+LOW+CLOSE)/4,52))/3; A7:=(EMA((OPEN+HIGH+LOW+CLOSE)/4,21)+EMA((OPEN+HIGH+LOW+CLOSE)/4,34)+EMA((OPEN +HIGH+LOW+CLOSE)/4,68))/3; VAR1:=FORCAST(A1,6); VAR2:=FORCAST(A2,6); VAR3:=FORCAST(A3,6); VAR4:=FORCAST(A4,6); VAR5:=FORCAST(A5,6);
[公文函的格式范文]公文函的格式及函的范 文 篇一: 公文函的格式及函的范文_公文格式范文 公文函的格式 因工作需要，以函的形式写一公文，所以在网上搜了一下函的相关内容： 函，即信；公函即公务信件。它是上下级和平行机关或不相隶属机关之间在商洽和联系工作、询问和答复问题时所使用的文体。函的特点是不受公文规定的严格限制，如不用正式文件头，也可不编文件号，有时还可不拟标题，因此用起来极为简便。 公函大体有以下几种用法：華勵志網 1、下级机关向上级机关询问一般事宜，或上级机关答复或催办下级机关有关事宜。 2、平行机关或不相隶属机关之间商洽有关事宜，
3、用函来通知一般事项。如通知开一般性的会议、要求下级机关报送某项材料或统计某些数字等时，也常用公函。 4、向上级机关请示较小事宜也常用函。 函件采用书写、复印、打印、传真等传递方式均可。 函的结构、内容和写法 由于函的类别较多，从制作格式到内容表述均有一定灵活机动性。主要介绍规范性公函的结构、内容和写法。 公函由首部、正文和尾部三部分组成。其各部分的格式、内容和写法要求如下： 首部。主要包括标题、主送机关两个项目内容。 1、标题。公函的标题一般有两种形式。一种是由发文机关名称、事由和文种构成。另一种是由事由和文种构成。華勵志網 2、主送机关。即受文并办理来函事项的机关单位，于文首顶格写明全称或者规范化简称，其后用冒号。
正文。其结构一般由开头、主体、结尾、结语等部分组成。 1、开头。主要说明发函的缘由。一般要求概括交代发函的目的、根据、原因等内容，然后用“现将有关问题说明如下：”或“现将有关事项函复如下：”等过渡语转入下文。复函的缘由部分，一般首先引叙来文的标题、发文字号，然后再交代根据，以说明发文的缘由。 2、主体。这是函的核心内容部分，主要说明致函事项。函的事项部分内容单一，一函一事，行文要直陈其事。无论是商洽工作，询问和答复问题，还是向有关主管部门请求批准事项等，都要用简洁得体的语言把需要告诉对方的问题、意见叙写清楚。如果属于复函，还要注意答复事项的针对性和明确性。 结尾。一般用礼貌性语言向对方提出希望。或请对方协助解决某一问题，或请对方及时复函，或请对方提出意见或请主管部门批准等。 结语。通常应根据函询、函告、函商或函复的事项，选择运用不同的结束语。如“特此函询”、“请即复函”、“特此函告”、“特此函复”等。有的函也可以不用结束语，如属便函，可以像普通信件一样，使用“此致”、“敬礼”。
2018届高三各地模拟考试作文及范文汇编XXXX年前加入自己朋友圈之后，就开始“极度关注”她的一举一动，“有些小情绪发着好玩，却被他们当了真，不断询问、劝说，让我哭笑不得。” 你的微信朋友圈是否对父母无权限开放？你是否愿意父母从朋友圈关注你的一举一动？当我们在朋友圈里屏蔽父母时。我们屏蔽的究竟是什么？ 也许是不想父母因为风吹草动就紧张兮兮，也许是不想父母因为一点儿小事就唠唠叨叨。也许，还有什么别的原因吧。 你是如何看待这个问题的？请结合材料，选好角度，确定立意，明确文体，自拟标题；不要套作，不得抄袭；不少于800字立意参考：材料的主要事件是“小刘悄悄在自己的微信朋友圈屏蔽了父母”，给出的任务是“你是如何看待这个问题的？”，可以审出要讨论“朋友圈该不该屏蔽父母”的问题。考生可从以下三个方面立意： 一、朋友圈应该屏蔽父母。 1．从孩子的角度立意：朋友圈有私密性；孩子渴望自由，渴望独立，避开父母，减少矛盾；只依靠朋友圈与父母交流，并不能使双方感情升温；部分屏蔽是最佳选择等； 2．从父母的角度可以立意：父母应尊重孩子，不要以爱的名义监督管控孩子；关心适度；过度的关心说教，让子女反感；父母与孩子之间存在代沟，会引发亲子误会，更不利于双方交流。 二、朋友圈不该屏蔽父母。
1．从孩子的角度立意：理解父母，不要让父母担心，试着换个方式交流，关心父母，搭建好与父母交流的桥梁； 2．从父母角度立意，父母想通过朋友圈了解孩子的生活、拉近与孩子的距离，建立家庭微信群，来加强沟通等。 三、朋友圈屏蔽父母折射出的社会问题1．孩子们报喜不报忧的心理2．父母与孩子之间的隔阂3．朋友圈里内容参差不齐等点睛：朋友圈该不该屏蔽父母，众说纷纭，莫衷一是。父母与孩子之间如何沟通，是一个常谈常新的话题，当这种交流遇上了朋友圈，就会发生更多的无法预知、无法想象、相似 但又不同的情况，现在的高中生新潮前卫、自尊敏感、个性张扬、兴趣广泛，对于这个话题一定有许多自己的看法。此次作文沿用任务驱动型作文模式，选材贴近学生生活，让所有考生都有话说。 父母：只想多一条途径了解孩子 “最近不知道怎么回事，女儿不怎么发朋友圈了？”昨日，市民黄女士不时掏出手机刷刷朋友圈，试图了解一下女儿陈玲的日常生活，却发现朋友圈依然没女儿发的消息。黄女士说，因为她和丈夫工作忙，女儿从小就很懂事独立。去年大学毕业后，女儿在工作单位附近买了一套小户型。此后，她回家的次数越来越少，给她打电话，她也总在忙。为了更好地跟女儿沟通，黄女士和丈夫特意加了女儿的微信，希望通过女儿的朋友圈动向了解她的生活、工作情况。但她发现，春节过后，女儿的微信朋友圈就基本处于零更新状态。“是不是节后上班太忙太累，或是心情不好，不愿发朋友圈？”黄女士打电话询问女
回复工作联络函范文工作联系函的格式格式： 函可以从不同角度分类： 函的格式：由于函的类别较多，从制作格式到内容表述均有一定灵活机动性。主要介绍规范性公函的结构、内容和写法。 首部。主要包括标题、主送机关两个项目内容。 正文。其结构一般由开头、主体、结尾、结语等部分组成。 结尾。一般用礼貌性语言向对方提出希望。或请对方协助解决某一问题，或请对方及时复函，或请对方提出意见或请主管部门批准等。 结语。通常应根据函询、函告、函商或函复的事项，选择运用不同的结束语。如“特此函询（商）”、“请即复函”、“特此函告”、“特此函复”等。有的函也可以不用结束语，如属便函，可以像普通信件一样，使用“此致”、“敬礼”。
结尾落款。一般包括署名和成文时间两项内容。署名机关单位名称，写明成文时间年、月、日；并加盖公章。 写作要点 准确。商务信函的内容多与双方的利益有着直接的利害关系，因而要完整、精确的表达意思，用于乃至标点符号都要做到准确无误，以免造成不必要的麻烦。 简洁。在做到准确、周到的前提下，应用最少的文字表达真实的意思，不能拖沓冗长。 具体。信函所要交待的事项必须具体明确，尤其要注意需要对方答复或会对双方关系产生影响的内容，绝不能语言不详。 礼貌。要掌握礼貌、得体的文字表达方式，以有利于双方保持良好的关系。 体谅。要学会换位思考，能够站在对方的立场上思考问题。这样容易获得对方的认同，有利于双方达成有效的沟通。 参考资料：
工作联系函一般用于各个公司之间相关事务的商讨，即公司之间的正式通信。一般发工作联系函都是一个公司有事要求另一个公司做，或者有什么问题需要联系对方公司，而工作联系函就相当于是正式的文件。电话联系固然可以，但是工作联系函才是最正式的方式，而且也方便双方记录。 工作联系函由首部、正文和尾部三部分组成。其各部分的格式、内容和写法要求如下： （一）首部。主要包括标题、主送机关两个项目内容。 1、标题。公函的标题一般有两种形式。一种是由发文机关名称、事由和文种构成。另一种是由事由和文种构成。 2、主送机关。即受文并办理来函事项的机关单位，于文首顶格写明全称或者规范化简称，其后用冒号。 （二）正文。其结构一般由开头、主体、结尾、结语等部分组成。
2019高考名校作文审题立意及范文汇编19-24题 19、阅读下面的材料，根据要求写作。（60分） “谁是英雄？今天，我们一起寻找”，众多媒体共同发起的“崇尚英雄精忠报国”大型网络互动活动点燃了亿万网民的英雄情结，“怎样的人称得上英雄”成为人们热议的话题。 为此，班上拟召开“怎样人称得上英雄”的主题班会，请你写一篇文章参与讨论。 要求：根据材料内容及含意，选好角度，确定立意；明确文体，自拟标题；不要套作，不得抄袭；不少于800字。 建立丰功伟业者是英雄，在平凡的岗位上默默奉献者也是英雄。只要围绕着“怎样的人称得上英雄”的话题进行写作即可。在语体要求上，注意是写“写一篇文章参与讨论”，对象是同学，语言表达要得体。 20、阅读下面的材料,按要求作文。(60分) 第三季《中国诗词大会》总决赛冠军是外卖小哥雷海为,他是这样读书的:书店读诗,回家默写;把等餐或等红绿灯的碎片时间用来读诗……身处喧嚣而心有诗意。 雷海为的对手、《中国成语大会》2015年度总冠军和《中国汉字听写大会》第三季第四现场年度总冠军、第三季《中国诗词大会》亚军彭敏是这样读书的:高中读、大学读,读到北大硕士……身处雅室而心自通灵。 对上述两种状态下的读书方式，你有什么看法?请结合你的学习经历和思考谈谈你的观点。要求:选好角度,自拟标题;不要套作,不得抄袭;不少于800字。 【文题解析】： 终身学习酿诗意。无论在何种情况下,保持着学习的热情、学习的渴望才是最重要的。正是这种学习方式,酿就了雷海为心中的诗意。……校园雅室的读书方式,奠定了终身学习的基础,但很多人却在校园学习之后泯然众人矣,何哉?就在于没有雷海为“外卖时学习”的方式。这种方式增加的不仅是知识,更锤炼“心”的韧性,在诗词大会与彭敏对决,其实他就是赢在社会的历练上！校园学习,让他知道了诗的魅力；而挤时间学习,则让他的生活充满诗意。 雅室读诗志趣高。汉字夺魁、成语夺冠、诗词屈居亚军,这就是北大硕士彭敏取得的成就!字、词、诗,涵盖了中文的三个要素,这样的成绩正是校园读书方式结下的硕果……在“喧嚣”中学习,采用“挤”的学习方式,固然能够酿诗意,但也限制了成长的深度和广度,只能在“诗岸”上徜徉!所以雅室学习条件更好更专注,理应更容易达成学习目标。 “雅室”“喧嚣”皆可读,读书岂能论环境。每个人的际遇不同,如何利用现有的环境及条件，坚定读书的心志,努力提升自己才是最重要的。“雅室”读书固然可喜,“喧嚣”读书亦能有成！在校园,亦应学海为;在杜会,也要仿彭敏。 【评分标准】 围绕“两种状态”下“两种学习方式”来展开议论,可以侧重其中一种,原则上不分高下，都属于切题作文；能辩证地谈两种方式的,或侧重一种但能辩证地涉及另一种的比较,可归入一类(48分) 21.阅读下面的材料，根据要求写作。 在冰雪茫茫的北方，有一个古老的民族，流传着一个古老的故事—— 一天晚上，老爷爷与孙子们围炉夜话，老爷爷说：“孩子们，在人们的内心深处，一直住着两只狼。这两只狼一直在进行着一场激烈的战斗。一只是恶狼，