NGS QC Toolkit: A Toolkit for Quality Control of Next Generation Sequencing Data

Next generation sequencing (NGS) technologies provide a high-throughput means to generate large amount of sequence data. However, quality control (QC) of sequence data generated from these technologies is extremely important for meaningful downstream analysis. Further, highly efficient and fast processing tools are required to handle the large volume of datasets. Here, we have developed an application, NGS QC Toolkit, for quality check and filtering of high-quality data. This toolkit is a standalone and open source application freely available at http://www.nipgr.res.in/ngsqctoolkit.html. All the tools in the application have been implemented in Perl programming language. The toolkit is comprised of user-friendly tools for QC of sequencing data generated using Roche 454 and Illumina platforms, and additional tools to aid QC (sequence format converter and trimming tools) and analysis (statistics tools). A variety of options have been provided to facilitate the QC at user-defined parameters. The toolkit is expected to be very useful for the QC of NGS data to facilitate better downstream analysis

Mining for Structural Variations in Next-Generation Sequencing Data

Genomic structural variations (SVs) are genetic alterations that result in duplications, insertions, deletions, inversions, and translocations of segments of DNA covering 50 or more base pairs. By changing the organization of DNA, SVs can contribute to phenotypic variation or cause pathological consequences as neurobehavioral disorders, autoimmune diseases, obesity, and cancers. SVs were first examined using classic cytogenetic methods, revealing changes down to 3 Mb. Later techniques for SV detection were based on array comparative genome hybridization (aCGH) and single-nucleotide polymorphism (SNP) arrays. Next-generation sequencing (NGS) approaches enabled precise characterization of breakpoints of SVs of various types and sizes at a genome-wide scale. Dissecting SVs from NGS presents substantial challenge due to the relatively short sequence reads and the large volume of the data. Benign variants and reference errors in the genome present another dimension of problem complexity. Even though a wide range of tools is available, the usage of SV callers in routine molecular diagnostic is still limited. SV detection algorithms relay on different properties of the underlying data and vary in accuracy and sensitivity; therefore, SV detection process usually utilizes multiple variant callers. This chapter summarizes strengths and limitations of different tools in effective NGS SV calling

EvoPipes.net: Bioinformatic Tools for Ecological and Evolutionary Genomics

Recent increases in the production of genomic data are yielding new opportunities and challenges for biologists. Among the chief problems posed by next-generation sequencing are assembly and analyses of these large data sets. Here we present an online server, http://EvoPipes.net, that provides access to a wide range of tools for bioinformatic analyses of genomic data oriented for ecological and evolutionary biologists. The EvoPipes.net server includes a basic tool kit for analyses of genomic data including a next-generation sequence cleaning pipeline (SnoWhite), scaffolded assembly software (SCARF), a reciprocal best-blast hit ortholog pipeline (RBH Orthologs), a pipeline for reference protein-based translation and identification of reading frame in transcriptome and genomic DNA (TransPipe), a pipeline to identify gene families and summarize the history of gene duplications (DupPipe), and a tool for developing SSRs or microsatellites from a transcriptome or genomic coding sequence collection (findSSR). EvoPipes.net also provides links to other software developed for evolutionary and ecological genomics, including chromEvol and NU-IN, as well as a forum for discussions of issues relating to genomic analyses and interpretation of results. Overall, these applications provide a basic bioinformatic tool kit that will enable ecologists and evolutionary biologists with relatively little experience and computational resources to take advantage of the opportunities provided by next-generation sequencing in their systems

Addressing challenges in the production and analysis of illumina sequencing data

Advances in DNA sequencing technologies have made it possible to generate large amounts of sequence data very rapidly and at substantially lower cost than capillary sequencing. These new technologies have specific characteristics and limitations that require either consideration during project design, or which must be addressed during data analysis. Specialist skills, both at the laboratory and the computational stages of project design and analysis, are crucial to the generation of high quality data from these new platforms. The Illumina sequencers (including the Genome Analyzers I/II/IIe/IIx and the new HiScan and HiSeq) represent a widely used platform providing parallel readout of several hundred million immobilized sequences using fluorescent-dye reversible-terminator chemistry. Sequencing library quality, sample handling, instrument settings and sequencing chemistry have a strong impact on sequencing run quality. The presence of adapter chimeras and adapter sequences at the end of short-insert molecules, as well as increased error rates and short read lengths complicate many computational analyses. We discuss here some of the factors that influence the frequency and severity of these problems and provide solutions for circumventing these. Further, we present a set of general principles for good analysis practice that enable problems with sequencing runs to be identified and dealt with

AdapterRemoval:easy cleaning of next generation sequencing reads

BACKGROUND: With the advent of next-generation sequencing there is an increased demand for tools to pre-process and handle the vast amounts of data generated. One recurring problem is adapter contamination in the reads, i.e. the partial or complete sequencing of adapter sequences. These adapter sequences have to be removed as they can hinder correct mapping of the reads and influence SNP calling and other downstream analyses. FINDINGS: We present a tool called AdapterRemoval which is able to pre-process both single and paired-end data. The program locates and removes adapter residues from the reads, it is able to combine paired reads if they overlap, and it can optionally trim low-quality nucleotides. Furthermore, it can look for adapter sequence in both the 5’ and 3’ ends of the reads. This is a flexible tool that can be tuned to accommodate different experimental settings and sequencing platforms producing FASTQ files. AdapterRemoval is shown to be good at trimming adapters from both single-end and paired-end data. CONCLUSIONS: AdapterRemoval is a comprehensive tool for analyzing next-generation sequencing data. It exhibits good performance both in terms of sensitivity and specificity. AdapterRemoval has already been used in various large projects and it is possible to extend it further to accommodate application-specific biases in the data

Suppression of artifacts and barcode bias in high-throughput transcriptome analyses utilizing template switching

Template switching (TS) has been an inherent mechanism of reverse transcriptase, which has been exploited in several transcriptome analysis methods, such as CAGE, RNA-Seq and short RNA sequencing. TS is an attractive option, given the simplicity of the protocol, which does not require an adaptor mediated step and thus minimizes sample loss. As such, it has been used in several studies that deal with limited amounts of RNA, such as in single cell studies. Additionally, TS has also been used to introduce DNA barcodes or indexes into different samples, cells or molecules. This labeling allows one to pool several samples into one sequencing flow cell, increasing the data throughput of sequencing and takes advantage of the increasing throughput of current sequences. Here, we report TS artifacts that form owing to a process called strand invasion. Due to the way in which barcodes/indexes are introduced by TS, strand invasion becomes more problematic by introducing unsystematic biases. We describe a strategy that eliminates these artifacts in silico and propose an experimental solution that suppresses biases from TS

SSP: An interval integer linear programming for de novo transcriptome assembly and isoform discovery of RNA-seq reads

AbstractRecent advances in the sequencing technologies have provided a handful of RNA-seq datasets for transcriptome analysis. However, reconstruction of full-length isoforms and estimation of the expression level of transcripts with a low cost are challenging tasks. We propose a novel de novo method named SSP that incorporates interval integer linear programming to resolve alternatively spliced isoforms and reconstruct the whole transcriptome from short reads. Experimental results show that SSP is fast and precise in determining different alternatively spliced isoforms along with the estimation of reconstructed transcript abundances. The SSP software package is available at http://www.bioinf.cs.ipm.ir/software/ssp