Performance comparison of five RNA-seq alignment tools

Aligning millions of short reads to a reference genome is a critical task in high throughput sequencing. In recent years, a large number of mapping algorithms have been developed, all of which have in common that they align a vast number of reads to genomic or transcriptomic sequences. RNA-Seq data is discrete in nature, therefore with reasonable gene models and comparative metrics RNA-Seq data can be simulated to sufficient accuracy to enable meaningful benchmarking of alignment algorithms. To provide guidance in the choice of alignment algorithms, five different alignment tools for RNA-Seq data are evaluated. In order to compare the accuracy and sensitivity of the Bowtie, Bowtie2, GMAP, Tophat and GNUMAP tools, their alignment accuracy for approximately 1 million simulated reads of chromosome one was evaluated using these five alignment tools. Bowtie has the highest accuracy, which is 92.42%, while GMAP has the lowest, which is 49.63%. Tophat has the highest sensitivity , which is 71.35% , while GMAP has the lowest, which is 51.69%

A comprehensive evaluation of alignment algorithms in the context of RNA-seq.

Transcriptome sequencing (RNA-Seq) overcomes limitations of previously used RNA quantification methods and provides one experimental framework for both high-throughput characterization and quantification of transcripts at the nucleotide level. The first step and a major challenge in the analysis of such experiments is the mapping of sequencing reads to a transcriptomic origin including the identification of splicing events. In recent years, a large number of such mapping algorithms have been developed, all of which have in common that they require algorithms for aligning a vast number of reads to genomic or transcriptomic sequences. Although the FM-index based aligner Bowtie has become a de facto standard within mapping pipelines, a much larger number of possible alignment algorithms have been developed also including other variants of FM-index based aligners. Accordingly, developers and users of RNA-seq mapping pipelines have the choice among a large number of available alignment algorithms. To provide guidance in the choice of alignment algorithms for these purposes, we evaluated the performance of 14 widely used alignment programs from three different algorithmic classes: algorithms using either hashing of the reference transcriptome, hashing of reads, or a compressed FM-index representation of the genome. Here, special emphasis was placed on both precision and recall and the performance for different read lengths and numbers of mismatches and indels in a read. Our results clearly showed the significant reduction in memory footprint and runtime provided by FM-index based aligners at a precision and recall comparable to the best hash table based aligners. Furthermore, the recently developed Bowtie 2 alignment algorithm shows a remarkable tolerance to both sequencing errors and indels, thus, essentially making hash-based aligners obsolete

EuPathDB: the eukaryotic pathogen database

ABSTRACT EuPathDB (http://eupathdb.org) resources include 11 databases supporting eukaryotic pathogen genomic and functional genomic data, isolate data and phylogenomics. EuPathDB resources are built using the same infrastructure and provide a sophisticated search strategy system enabling complex interrogations of underlying data. Recent advances in EuPathDB resources include the design and implementation of a new data loading workflow, a new database supporting Piroplasmida (i.e. Babesia and Theileria), the addition of large amounts of new data and data types and the incorporation of new analysis tools. New data include genome sequences and annotation, strand-specific RNA-seq data, splice junction predictions (based on RNAseq), phosphoproteomic data, high-throughput phenotyping data, single nucleotide polymorphism data based on high-throughput sequencing (HTS) and expression quantitative trait loci data. New analysis tools enable users to search for DNA motifs and define genes based on their genomic colocation, view results from searches graphically (i.e. genes mapped to chromosomes or isolates displayed on a map) and analyze data from columns in result tables (word cloud and histogram summaries of column content). The manuscript herein describes updates to EuPathDB since the previous report published in NAR in 2010

SeqAssist: A Novel Toolkit For Preliminary Analysis of Next-Generation Sequencing Data

Background: While next-generation sequencing (NGS) technologies are rapidly advancing, an area that lags behind is the development of efficient and user-friendly tools for preliminary analysis of massive NGS data. As an effort to fill this gap to keep up with the fast pace of technological advancement and to accelerate data-to-results turnaround, we developed a novel software package named SeqAssist ( Sequencing Assistant or SA). Results: SeqAssist takes NGS-generated FASTQ files as the input, employs the BWA-MEM aligner for sequence alignment, and aims to provide a quick overview and basic statistics of NGS data. It consists of three separate workflows: (1) the SA_RunStats workflow generates basic statistics about an NGS dataset, including numbers of raw, cleaned, redundant and unique reads, redundancy rate, and a list of unique sequences with length and read count; (2) the SA_Run2Ref workflow estimates the breadth, depth and evenness of genome-wide coverage of the NGS dataset at a nucleotide resolution; and (3) the SA_Run2Run workflow compares two NGS datasets to determine the redundancy (overlapping rate) between the two NGS runs. Statistics produced by SeqAssist or derived from SeqAssist output files are designed to inform the user: whether, what percentage, how many times and how evenly a genomic locus (i.e., gene, scaffold, chromosome or genome) is covered by sequencing reads, how redundant the sequencing reads are in a single run or between two runs. These statistics can guide the user in evaluating the quality of a DNA library prepared for RNA-Seq or genome (re-)sequencing and in deciding the number of sequencing runs required for the library. We have tested SeqAssist using a synthetic dataset and demonstrated its main features using multiple NGS datasets generated from genome re-sequencing experiments. Conclusions: SeqAssist is a useful and informative tool that can serve as a valuable assistant to a broad range of investigators who conduct genome re-sequencing, RNA-Seq, or de novo genome sequencing and assembly experiments

iREAD: a tool for intron retention detection from RNA-seq data.

BACKGROUND: Intron retention (IR) has been traditionally overlooked as \u27noise\u27 and received negligible attention in the field of gene expression analysis. In recent years, IR has become an emerging field for interrogating transcriptomes because it has been recognized to carry out important biological functions such as gene expression regulation and it has been found to be associated with complex diseases such as cancers. However, methods for detecting IR today are limited. Thus, there is a need to develop novel methods to improve IR detection. RESULTS: Here we present iREAD (intron REtention Analysis and Detector), a tool to detect IR events genome-wide from high-throughput RNA-seq data. The command line interface for iREAD is implemented in Python. iREAD takes as input a BAM file, representing the transcriptome, and a text file containing the intron coordinates of a genome. It then 1) counts all reads that overlap intron regions, 2) detects IR events by analyzing the features of reads such as depth and distribution patterns, and 3) outputs a list of retained introns into a tab-delimited text file. iREAD provides significant added value in detecting IR compared with output from IRFinder with a higher AUC on all datasets tested. Both methods showed low false positive rates and high false negative rates in different regimes, indicating that use together is generally beneficial. The output from iREAD can be directly used for further exploratory analysis such as differential intron expression and functional enrichment. The software is freely available at https://github.com/genemine/iread. CONCLUSION: Being complementary to existing tools, iREAD provides a new and generic tool to interrogate poly-A enriched transcriptomic data of intron regions. Intron retention analysis provides a complementary approach for understanding transcriptome

State of art fusion-finder algorithms are suitable to detect transcription-induced chimeras in normal tissues?

BACKGROUND: RNA-seq has the potential to discover genes created by chromosomal rearrangements. Fusion genes, also known as "chimeras", are formed by the breakage and re-joining of two different chromosomes. It is known that chimeras have been implicated in the development of cancer. Few publications in the past showed the presence of fusion events also in normal tissue, but with very limited overlaps between their results. More recently, two fusion genes in normal tissues were detected using both RNA-seq and protein data. Due to heterogeneous results in identifying chimeras in normal tissue, we decided to evaluate the efficacy of state of the art fusion finders in detecting chimeras in RNA-seq data from normal tissues. RESULTS: We compared the performance of six fusion-finder tools: FusionHunter, FusionMap, FusionFinder, MapSplice, deFuse and TopHat-fusion. To evaluate the sensitivity we used a synthetic dataset of fusion-products, called positive dataset; in these experiments FusionMap, FusionFinder, MapSplice, and TopHat-fusion are able to detect more than 78% of fusion genes. All tools were error prone with high variability among the tools, identifying some fusion genes not present in the synthetic dataset. To better investigate the false discovery chimera detection rate, synthetic datasets free of fusion-products, called negative datasets, were used. The negative datasets have different read lengths and quality scores, which allow detecting dependency of the tools on both these features. FusionMap, FusionFinder, mapSplice, deFuse and TopHat-fusion were error-prone. Only FusionHunter results were free of false positive. FusionMap gave the best compromise in terms of specificity in the negative dataset and of sensitivity in the positive dataset. CONCLUSIONS: We have observed a dependency of the tools on read length, quality score and on the number of reads supporting each chimera. Thus, it is important to carefully select the software on the basis of the structure of the RNA-seq data under analysis. Furthermore, the sensitivity of chimera detection tools does not seem to be sufficient to provide results consistent with those obtained in normal tissues on the basis of fusion events extracted from published data

A context-based approach to identify the most likely mapping for RNA-seq experiments

Background: Sequencing of mRNA (RNA-seq) by next generation sequencing technologies is widely used for analyzing the transcriptomic state of a cell. Here, one of the main challenges is the mapping of a sequenced read to its transcriptomic origin. As a simple alignment to the genome will fail to identify reads crossing splice junctions and a transcriptome alignment will miss novel splice sites, several approaches have been developed for this purpose. Most of these approaches have two drawbacks. First, each read is assigned to a location independent on whether the corresponding gene is expressed or not, i.e. information from other reads is not taken into account. Second, in case of multiple possible mappings, the mapping with the fewest mismatches is usually chosen which may lead to wrong assignments due to sequencing errors. Results: To address these problems, we developed ContextMap which efficiently uses information on the context of a read, i.e. reads mapping to the same expressed region. The context information is used to resolve possible ambiguities and, thus, a much larger degree of ambiguities can be allowed in the initial stage in order to detect all possible candidate positions. Although ContextMap can be used as a stand-alone version using either a genome or transcriptome as input, the version presented in this article is focused on refining initial mappings provided by other mapping algorithms. Evaluation results on simulated sequencing reads showed that the application of ContextMap to either TopHat or MapSplice mappings improved the mapping accuracy of both initial mappings considerably. Conclusions: In this article, we show that the context of reads mapping to nearby locations provides valuable information for identifying the best unique mapping for a read. Using our method, mappings provided by other state-of-the-art methods can be refined and alignment accuracy can be further improved

iMapSplice: Alleviating Reference Bias Through Personalized RNA-seq Alignment

Genomic variants in both coding and non-coding sequences can have functionally important and sometimes deleterious effects on exon splicing of gene transcripts. For transcriptome profiling using RNA-seq, the accurate alignment of reads across exon junctions is a critical step. Existing algorithms that utilize a standard reference genome as a template sometimes have difficulty in mapping reads that carry genomic variants. These problems can lead to allelic ratio biases and the failure to detect splice variants created by splice site polymorphisms. To improve RNA-seq read alignment, we have developed a novel approach called iMapSplice that enables personalized mRNA transcriptome profiling. The algorithm makes use of personal genomic information and performs an unbiased alignment towards genome indices carrying both reference and alternative bases. Importantly, this breaks the dependency on reference genome splice site dinucleotide motifs and enables iMapSplice to discover personal splice junctions created through splice site polymorphisms. We report comparative analyses using a number of simulated and real datasets. Besides general improvements in read alignment and splice junction discovery, iMapSplice greatly alleviates allelic ratio biases and unravels many previously uncharacterized splice junctions created by splice site polymorphisms, with minimal overhead in computation time and storage. Software download URL: https://github.com/LiuBioinfo/iMapSplice

Involvement of Innate Immune System in Late Stages of Inherited Photoreceptor Degeneration

Retinitis pigmentosa (RP) is a group of inherited retinal degenerations that lead to progressive vision loss. Over 200 mutations in 60 different genes have been shown to cause RP. Given the diversity of genes and mutations that cause RP, corrective gene therapy approaches currently in development may prove both time-consuming and cost-prohibitive for treatment of all forms of RP. An alternative approach is to find common biological pathways that cause retinal degeneration in various forms of RP, and identify new molecular targets. With this goal, we analyzed the retinal transcriptome of two non-allelic forms of RP in dogs, rcd1 and xlpra2, at clinically relevant advanced stages of the two diseases. Both diseases showed very similar trends in changes in gene expression compared to control normal dogs. Pathway analysis revealed upregulation of various components of the innate immune system in both diseases, including inflammasome and complement pathways. Our results show that the retinal transcriptome at advanced stages of RP is very similar to that of other retinal degenerative diseases such as age-related macular degeneration and diabetic retinopathy. Thus, drugs and therapeutics already in development for targeting these retinopathies may also prove useful for the treatment of many forms of RP