Benchmarking RNA-Seq Aligners at Base-Level and Junction Base-Level Resolution Using the Arabidopsis thaliana Genome

The utmost goal of selecting an RNA-Seq alignment software is to perform accurate alignments with a robust algorithm, which is capable of detecting the various intricacies underlying read-mapping procedures and beyond. Most alignment software tools are typically pre-tuned with human or prokaryotic data, and therefore may not be suitable for applications to other organisms, such as plants. The rapidly growing plant RNA-Seq databases call for the assessment of the alignment tools on curated plant data, which will aid the calibration of these tools for applications to plant transcriptomic data. We therefore focused here on benchmarking RNA-Seq read alignment tools, using simulated data derived from the model organism Arabidopsis thaliana. We assessed the performance of five popular RNA-Seq alignment tools that are currently available, based on their usage (citation count). By introducing annotated single nucleotide polymorphisms (SNPs) from The Arabidopsis Information Resource (TAIR), we recorded alignment accuracy at both base-level and junction base-level resolutions for each alignment tool. In addition to assessing the performance of the alignment tools at their default settings, accuracies were also recorded by varying the values of numerous parameters, including the confidence threshold and the level of SNP introduction. The performances of the aligners were found consistent under various testing conditions at the base-level accuracy; however, the junction base-level assessment produced varying results depending upon the applied algorithm. At the read base-level assessment, the overall performance of the aligner STAR was superior to other aligners, with the overall accuracy reaching over 90% under different test conditions. On the other hand, at the junction base-level assessment, SubRead emerged as the most promising aligner, with an overall accuracy over 80% under most test conditions.

Chromatin structure can introduce systematic biases in genome-wide analyses of Plasmodium falciparum

Background: The maintenance, regulation, and dynamics of heterochromatin in the human malaria parasite, Plasmodium falciparum, has drawn increasing attention due to its regulatory role in mutually exclusive virulence gene expression and the silencing of key developmental regulators. The advent of genome-wide analyses such as chromatin-immunoprecipitation followed by sequencing (ChIP-seq) has been instrumental in understanding chromatin composition; however, even in model organisms, ChIP-seq experiments are susceptible to intrinsic experimental biases arising from underlying chromatin structure. Methods: We performed a control ChIP-seq experiment, re-analyzed previously published ChIP-seq datasets and compared different analysis approaches to characterize biases of genome-wide analyses in P. falciparum. Results: We found that heterochromatic regions in input control samples used for ChIP-seq normalization are systematically underrepresented in regard to sequencing coverage across the P. falciparum genome. This underrepresentation, in combination with a non-specific or inefficient immunoprecipitation, can lead to the identification of false enrichment and peaks across these regions. We observed that such biases can also be seen at background levels in specific and efficient ChIP-seq experiments. We further report on how different read mapping approaches can also skew sequencing coverage within highly similar subtelomeric regions and virulence gene families. To ameliorate these issues, we discuss orthogonal methods that can be used to characterize bona fide chromatin-associated proteins. Conclusions: Our results highlight the impact of chromatin structure on genome-wide analyses in the parasite and the need for caution when characterizing chromatin-associated proteins and features.

Fast and memory efficient approach for mapping NGS reads to a reference genome

New generation sequencing machines: Illumina and Solexa can generate millions of short reads from a given genome sequence on a single run. Alignment of these reads to a reference genome is a core step in Next-generation sequencing data analysis such as genetic variation and genome re-sequencing etc. Therefore there is a need of a new approach, efficient with respect to memory as well as time to align these enormous reads with the reference genome. Existing techniques such as MAQ, Bowtie, BWA, BWBBLE, Subread, Kart, and Minimap2 require huge memory for whole reference genome indexing and reads alignment. Gapped alignment versions of these techniques are also 20-40% slower than their respective normal versions. In this paper, an efficient approach: WIT for reference genome indexing and reads alignment using Burrows-Wheeler Transform (BWT) and Wavelet Tree (WT) is proposed. Both exact and approximate alignments are possible by it. Experimental work shows that the proposed approach WIT performs the best in case of protein sequence indexing. For indexing, the reference genome space required by WIT is 0.6 N (N is the size of reference genome) whereas existing techniques BWA, Subread, Kart, and Minimap2 require space in between 1.25 N to 5 N. Experimentally, it is also observed that even using such small index size alignment time of proposed approach is comparable in comparison to BWA, Subread, Kart, and Minimap2. Other alignment parameters accuracy and confidentiality are also experimentally shown to be better than Minimap2. The source code of the proposed approach WIT is available at http://www.algorithm-skg.com/wit/home.html .

Tackling the Challenges of FASTQ Referential Compression

The exponential growth of genomic data has recently motivated the development of compression algorithms to tackle the storage capacity limitations in bioinformatics centers. Referential compressors could theoretically achieve a much higher compression than their non-referential counterparts; however, the latest tools have not been able to harness such potential yet. To reach such goal, an efficient encoding model to represent the differences between the input and the reference is needed. In this article, we introduce a novel approach for referential compression of FASTQ files. The core of our compression scheme consists of a referential compressor based on the combination of local alignments with binary encoding optimized for long reads. Here we present the algorithms and performance tests developed for our reads compression algorithm, named UdeACompress. Our compressor achieved the best results when compressing long reads and competitive compression ratios for shorter reads when compared to the best programs in the state of the art. As an added value, it also showed reasonable execution times and memory consumption, in comparison with similar tools.

From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data

Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely used to identify the genomic binding sites for protein of interest. Most conventional approaches to ChIP-seq data analysis involve the detection of the absolute presence (or absence) of a binding site. However, an alternative strategy is to identify changes in the binding intensity between two biological conditions, i.e., differential binding (DB). This may yield more relevant results than conventional analyses, as changes in binding can be associated with the biological difference being investigated. The aim of this article is to facilitate the implementation of DB analyses, by comprehensively describing a computational workflow for the detection of DB regions from ChIP-seq data. The workflow is based primarily on R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, from alignment of read sequences to interpretation and visualization of putative DB regions. In particular, detection of DB regions will be conducted using the counts for sliding windows from the csaw package, with statistical modelling performed using methods in the edgeR package. Analyses will be demonstrated on real histone mark and transcription factor data sets. This will provide readers with practical usage examples that can be applied in their own studies.

From reads to regions: a Bioconductor workflow to detect differential binding in ChIP-seq data

Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely used to identify the genomic binding sites for protein of interest. Most conventional approaches to ChIP-seq data analysis involve the detection of the absolute presence (or absence) of a binding site. However, an alternative strategy is to identify changes in the binding intensity between two biological conditions, i.e., differential binding (DB). This may yield more relevant results than conventional analyses, as changes in binding can be associated with the biological difference being investigated. The aim of this article is to facilitate the implementation of DB analyses, by comprehensively describing a computational workflow for the detection of DB regions from ChIP-seq data. The workflow is based primarily on R software packages from the open-source Bioconductor project and covers all steps of the analysis pipeline, from alignment of read sequences to interpretation and visualization of putative DB regions. In particular, detection of DB regions will be conducted using the counts for sliding windows from the csaw package, with statistical modelling performed using methods in the edgeR package. Analyses will be demonstrated on real histone mark and transcription factor data sets. This will provide readers with practical usage examples that can be applied in their own studies.

BM-map: Bayesian mapping of multireads for next-generation sequencing data

Next-generation sequencing (NGS) technology generates millions of short reads, which provide valuable information for various aspects of cellular activities and biological functions. A key step in NGS applications (e.g., RNA-Seq) is to map short reads to correct genomic locations within the source genome. While most reads are mapped to a unique location, a significant proportion of reads align to multiple genomic locations with equal or similar numbers of mismatches; these are called multireads. The ambiguity in mapping the multireads may lead to bias in downstream analyses. Currently, most practitioners discard the multireads in their analysis, resulting in a loss of valuable information, especially for the genes with similar sequences. To refine the read mapping, we develop a Bayesian model that computes the posterior probability of mapping a multiread to each competing location. The probabilities are used for downstream analyses, such as the quantification of gene expression. We show through simulation studies and RNA-Seq analysis of real life data that the Bayesian method yields better mapping than the current leading methods. We provide a C++ program for downloading that is being packaged into a user-friendly software.