Yanagi: Transcript Segment Library Construction for RNA-Seq Quantification

Analysis of differential alternative splicing from RNA-seq data is complicated by the fact that many RNA-seq reads map to multiple transcripts, and that annotated transcripts from a given gene are often a small subset of many possible complete transcripts for that gene. Here we describe Yanagi, a tool which segments a transcriptome into disjoint regions to create a segments library from a complete transcriptome annotation that preserves all of its consecutive regions of a given length L while distinguishing annotated alternative splicing events in the transcriptome. In this paper, we formalize this concept of transcriptome segmentation and propose an efficient algorithm for generating segment libraries based on a length parameter dependent on specific RNA-Seq library construction. The resulting segment sequences can be used with pseudo-alignment tools to quantify expression at the segment level. We characterize the segment libraries for the reference transcriptomes of Drosophila melanogaster and Homo sapiens. Finally, we demonstrate the utility of quantification using a segment library based on an analysis of differential exon skipping in Drosophila melanogaster and Homo sapiens. The notion of transcript segmentation as introduced here and implemented in Yanagi will open the door for the application of lightweight, ultra-fast pseudo-alignment algorithms in a wide variety of analyses of transcription variation

A computational survey of candidate exonic splicing enhancer motifs in the model plant Arabidopsis thaliana

Algorithmic approaches to splice site prediction have relied mainly on the consensus patterns found at the boundaries between protein coding and non-coding regions. However exonic splicing enhancers have been shown to enhance the utilization of nearby splice sites. We have developed a new computational technique to identify significantly conserved motifs involved in splice site regulation. First, 84 putative exonic splicing enhancer hexamers are identified in Arabidopsis thaliana. Then a Gibbs sampling program called ELPH was used to locate conserved motifs represented by these hexamers in exonic regions near splice sites in confirmed genes. Oligomers containing 35 of these motifs have been shown experimentally to induce significant inclusion of A. thaliana exons. Second, integration of our regulatory motifs into two different splice site recognition programs significantly improved the ability of the software to correctly predict splice sites in a large database of confirmed genes. We have released GeneSplicerESE, the improved splice site recognition code, as open source software. Our results show that the use of the ESE motifs consistently improves splice site prediction accuracy.https://doi.org/10.1186/1471-2105-8-15

Evolutionary dynamics of U12-type spliceosomal introns

<p>Abstract</p> <p>Background</p> <p>Many multicellular eukaryotes have two types of spliceosomes for the removal of introns from messenger RNA precursors. The major (U2) spliceosome processes the vast majority of introns, referred to as U2-type introns, while the minor (U12) spliceosome removes a small fraction (less than 0.5%) of introns, referred to as U12-type introns. U12-type introns have distinct sequence elements and usually occur together in genes with U2-type introns. A phylogenetic distribution of U12-type introns shows that the minor splicing pathway appeared very early in eukaryotic evolution and has been lost repeatedly.</p> <p>Results</p> <p>We have investigated the evolution of U12-type introns among eighteen metazoan genomes by analyzing orthologous U12-type intron clusters. Examination of gain, loss, and type switching shows that intron type is remarkably conserved among vertebrates. Among 180 intron clusters, only eight show intron loss in any vertebrate species and only five show conversion between the U12 and the U2-type. Although there are only nineteen U12-type introns in <it>Drosophila melanogaster</it>, we found one case of U2 to U12-type conversion, apparently mediated by the activation of cryptic U12 splice sites early in the dipteran lineage. Overall, loss of U12-type introns is more common than conversion to U2-type and the U12 to U2 conversion occurs more frequently among introns of the GT-AG subtype than among introns of the AT-AC subtype. We also found support for natural U12-type introns with non-canonical terminal dinucleotides (CT-AC, GG-AG, and GA-AG) that have not been previously reported.</p> <p>Conclusions</p> <p>Although complete loss of the U12-type spliceosome has occurred repeatedly, U12 introns are extremely stable in some taxa, including eutheria. Loss of U12 introns or the genes containing them is more common than conversion to the U2-type. The degeneracy of U12-type terminal dinucleotides among natural U12-type introns is higher than previously thought.</p

SplicePort—An interactive splice-site analysis tool

SplicePort is a web-based tool for splice-site analysis that allows the user to make splice-site predictions for submitted sequences. In addition, the user can also browse the rich catalog of features that underlies these predictions, and which we have found capable of providing high classification accuracy on human splice sites. Feature selection is optimized for human splice sites, but the selected features are likely to be predictive for other mammals as well. With our interactive feature browsing and visualization tool, the user can view and explore subsets of features used in splice-site prediction (either the features that account for the classification of a specific input sequence or the complete collection of features). Selected feature sets can be searched, ranked or displayed easily. The user can group features into clusters and frequency plot WebLogos can be generated for each cluster. The user can browse the identified clusters and their contributing elements, looking for new interesting signals, or can validate previously observed signals. The SplicePort web server can be accessed at http://www.cs.umd.edu/projects/SplicePort and http://www.spliceport.org

Yanagi: Fast and interpretable segment-based alternative splicing and gene expression analysis

Ultra-fast pseudo-alignment approaches are the tool of choice in transcript-level RNA sequencing (RNA-seq) analyses. Unfortunately, these methods couple the tasks of pseudo-alignment and transcript quantification. This coupling precludes the direct usage of pseudo-alignment to other expression analyses, including alternative splicing or differential gene expression analysis, without including a non-essential transcript quantification step.https://doi.org/10.1186/s12859-019-2947-

READABILITY ASSESSMENT ON THE TRANSLATION OF USER MANUAL OF SAMSUNG GALAXY TAB 2 7.0 AND THE FACTORS INFLUENCING IT

We introduce Sailfish, a computational method for quantifying the abundance of previously annotated RNA isoforms from RNA-seq data. Because Sailfish entirely avoids mapping reads, a time-consuming step in all current methods, it provides quantification estimates much faster than do existing approaches (typically 20 times faster) without loss of accuracy. By facilitating frequent reanalysis of data and reducing the need to optimize parameters, Sailfish exemplifies the potential of lightweight algorithms for efficiently processing sequencing reads.</p

Dynamic Querying for Pattern Identification in Microarray and Genomic Data (2003)

Data sets involving linear ordered sequences are a recurring theme in bioinformatics. Dynamic query tools that support exploration of these data sets can be useful for identifying patterns of interest. This paper describes the use of one such tool TimeSearcher - to interactively explore linear sequence data sets taken from two bioinformatics problems. Microarray time course data sets involve expression levels for large numbers of genes over multiple time points. TimeSearcher can be used to interactively search these data sets for genes with expression profiles of interest. The occurrence frequencies of short sequences of DNA in aligned exons can be used to identify sequences that play a role in the pre-mRNA splicing. TimeSearcher can be used to search these data sets for candidate splicing signals

Insights from GWAS: emerging landscape of mechanisms underlying complex trait disease

There are now over 2000 loci in the human genome where genome wide association studies (GWAS) have found one or more SNPs to be associated with altered risk of a complex trait disease. At each of these loci, there must be some molecular level mechanism relevant to the disease. What are these mechanisms and how do they contribute to disease? Here we consider the roles of three primary mechanism classes: changes that directly alter protein function (missense SNPs), changes that alter transcript abundance as a consequence of variants close-by in sequence, and changes that affect splicing. Missense SNPs are divided into those predicted to have a high impact on in vivo protein function, and those with a low impact. Splicing is divided into SNPs with a direct impact on splice sites, and those with a predicted effect on auxiliary splicing signals. The analysis was based on associations found for seven complex trait diseases in the classic Wellcome Trust Case Control Consortium (WTCCC1) GWA study and subsequent studies and meta-analyses, collected from the GWAS catalog. Linkage disequilibrium information was used to identify possible candidate SNPs for involvement in disease mechanism in each of the 356 loci associated with these seven diseases. With the parameters used, we find that 76% of loci have at least of these mechanisms. Overall, except for the low incidence of direct impact on splice sites, the mechanisms are found at similar frequencies, with changes in transcript abundance the most common. But the distribution of mechanisms over diseases varies markedly, as does the fraction of loci with assigned mechanisms. Many of the implicated proteins have previously been suggested as relevant, but the specific mechanism assignments are new. In addition, a number of new disease relevant proteins are proposed. The high fraction of GWAS loci with proposed mechanisms suggests that these classes of mechanism play a major role. Other mechanism types, such as variants affecting expression of genes remote in the DNA sequence, will contribute in other loci. Each of the identified putative mechanisms provides a hypothesis for further investigation.https://doi.org/10.1186/1471-2164-16-S8-S

Evaluation of BLAST-based edge-weighting metrics used for homology inference with the Markov Clustering algorithm

Clustering protein sequences according to inferred homology is a fundamental step in the analysis of many large data sets. Since the publication of the Markov Clustering (MCL) algorithm in 2002, it has been the centerpiece of several popular applications. Each of these approaches generates an undirected graph that represents sequences as nodes connected to each other by edges weighted with a BLAST-based metric. MCL is then used to infer clusters of homologous proteins by analyzing these graphs. The various approaches differ only by how they weight the edges, yet there has been very little direct examination of the relative performance of alternative edge-weighting metrics. This study compares the performance of four BLAST-based edge-weighting metrics: the bit score, bit score ratio (BSR), bit score over anchored length (BAL), and negative common log of the expectation value (NLE). Performance is tested using the Extended CEGMA KOGs (ECK) database, which we introduce here. All metrics performed similarly when analyzing full-length sequences, but dramatic differences emerged as progressively larger fractions of the test sequences were split into fragments. The BSR and BAL successfully rescued subsets of clusters by strengthening certain types of alignments between fragmented sequences, but also shifted the largest correct scores down near the range of scores generated from spurious alignments. This penalty outweighed the benefits in most test cases, and was greatly exacerbated by increasing the MCL inflation parameter, making these metrics less robust than the bit score or the more popular NLE. Notably, the bit score performed as well or better than the other three metrics in all scenarios. The results provide a strong case for use of the bit score, which appears to offer equivalent or superior performance to the more popular NLE. The insight that MCL-based clustering methods can be improved using a more tractable edge-weighting metric will greatly simplify future implementations. We demonstrate this with our own minimalist Python implementation: Porthos, which uses only standard libraries and can process a graph with 25 m + edges connecting the 60 k + KOG sequences in half a minute using less than half a gigabyte of memory.https://doi.org/10.1186/s12859-015-0625-xhttps://doi.org/10.1186/s12859-015-0690-