Rapid evaluation and quality control of next generation sequencing data with FaQCs

Background

Next generation sequencing (NGS) technologies that parallelize the sequencing process and produce thousands to millions, or even hundreds of millions of sequences in a single sequencing run, have revolutionized genomic and genetic research. Because of the vagaries of any platform’s sequencing chemistry, the experimental processing, machine failure, and so on, the quality of sequencing reads is never perfect, and often declines as the read is extended. These errors invariably affect downstream analysis/application and should therefore be identified early on to mitigate any unforeseen effects.

Results

Here we present a novel FastQ Quality Control Software (FaQCs) that can rapidly process large volumes of data, and which improves upon previous solutions to monitor the quality and remove poor quality data from sequencing runs. Both the speed of processing and the memory footprint of storing all required information have been optimized via algorithmic and parallel processing solutions. The trimmed output compared side-by-side with the original data is part of the automated PDF output. We show how this tool can help data analysis by providing a few examples, including an increased percentage of reads recruited to references, improved single nucleotide polymorphism identification as well as de novo sequence assembly metrics.

Conclusion

FaQCs combines several features of currently available applications into a single, user-friendly process, and includes additional unique capabilities such as filtering the PhiX control sequences, conversion of FASTQ formats, and multi-threading. The original data and trimmed summaries are reported within a variety of graphics and reports, providing a simple way to do data quality control and assurance.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0366-2) contains supplementary material, which is available to authorized users.     

Analysis of archived residual newborn screening blood spots after whole genome amplification

Background

Deidentified newborn screening bloodspot samples (NBS) represent a valuable potential resource for genomic research if impediments to whole exome sequencing of NBS deoxyribonucleic acid (DNA), including the small amount of genomic DNA in NBS material, can be overcome. For instance, genomic analysis of NBS could be used to define allele frequencies of disease-associated variants in local populations, or to conduct prospective or retrospective studies relating genomic variation to disease emergence in pediatric populations over time. In this study, we compared the recovery of variant calls from exome sequences of amplified NBS genomic DNA to variant calls from exome sequencing of non-amplified NBS DNA from the same individuals.

Results

Using a standard alignment-based Genome Analysis Toolkit (GATK), we find 62,000–76,000 additional variants in amplified samples. After application of a unique kmer enumeration and variant detection method (RUFUS), only 38,000–47,000 additional variants are observed in amplified gDNA. This result suggests that roughly half of the amplification-introduced variants identified using GATK may be the result of mapping errors and read misalignment.

Conclusions

Our results show that it is possible to obtain informative, high-quality data from exome analysis of whole genome amplified NBS with the important caveat that different data generation and analysis methods can affect variant detection accuracy, and the concordance of variant calls in whole-genome amplified and non-amplified exomes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1747-2) contains supplementary material, which is available to authorized users.     

RIEMS: a software pipeline for sensitive and comprehensive taxonomic classification of reads from metagenomics datasets

Background

Fuelled by the advent and subsequent development of next generation sequencing technologies, metagenomics became a powerful tool for the analysis of microbial communities both scientifically and diagnostically. The biggest challenge is the extraction of relevant information from the huge sequence datasets generated for metagenomics studies. Although a plethora of tools are available, data analysis is still a bottleneck.

Results

To overcome the bottleneck of data analysis, we developed an automated computational workflow called RIEMS – Reliable Information Extraction from Metagenomic Sequence datasets. RIEMS assigns every individual read sequence within a dataset taxonomically by cascading different sequence analyses with decreasing stringency of the assignments using various software applications. After completion of the analyses, the results are summarised in a clearly structured result protocol organised taxonomically. The high accuracy and performance of RIEMS analyses were proven in comparison with other tools for metagenomics data analysis using simulated sequencing read datasets.

Conclusions

RIEMS has the potential to fill the gap that still exists with regard to data analysis for metagenomics studies. The usefulness and power of RIEMS for the analysis of genuine sequencing datasets was demonstrated with an early version of RIEMS in 2011 when it was used to detect the orthobunyavirus sequences leading to the discovery of Schmallenberg virus.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0503-6) contains supplementary material, which is available to authorized users.     

Variant detection sensitivity and biases in whole genome and exome sequencing

Background

Less than two percent of the human genome is protein coding, yet that small fraction harbours the majority of known disease causing mutations. Despite rapidly falling whole genome sequencing (WGS) costs, much research and increasingly the clinical use of sequence data is likely to remain focused on the protein coding exome. We set out to quantify and understand how WGS compares with the targeted capture and sequencing of the exome (exome-seq), for the specific purpose of identifying single nucleotide polymorphisms (SNPs) in exome targeted regions.

Results

We have compared polymorphism detection sensitivity and systematic biases using a set of tissue samples that have been subject to both deep exome and whole genome sequencing. The scoring of detection sensitivity was based on sequence down sampling and reference to a set of gold-standard SNP calls for each sample. Despite evidence of incremental improvements in exome capture technology over time, whole genome sequencing has greater uniformity of sequence read coverage and reduced biases in the detection of non-reference alleles than exome-seq. Exome-seq achieves 95% SNP detection sensitivity at a mean on-target depth of 40 reads, whereas WGS only requires a mean of 14 reads. Known disease causing mutations are not biased towards easy or hard to sequence areas of the genome for either exome-seq or WGS.

Conclusions

From an economic perspective, WGS is at parity with exome-seq for variant detection in the targeted coding regions. WGS offers benefits in uniformity of read coverage and more balanced allele ratio calls, both of which can in most cases be offset by deeper exome-seq, with the caveat that some exome-seq targets will never achieve sufficient mapped read depth for variant detection due to technical difficulties or probe failures. As WGS is intrinsically richer data that can provide insight into polymorphisms outside coding regions and reveal genomic rearrangements, it is likely to progressively replace exome-seq for many applications.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-247) contains supplementary material, which is available to authorized users.     

Allelic mapping bias in RNA-sequencing is not a major confounder in eQTL studies

Background

RNA sequencing (RNA-seq) is the current gold-standard method to quantify gene expression for expression quantitative trait locus (eQTL) studies. However, a potential caveat in these studies is that RNA-seq reads carrying the non-reference allele of variant loci can have lower probability to map correctly to the reference genome, which could bias gene quantifications and cause false positive eQTL associations. In this study, we analyze the effect of this allelic mapping bias in eQTL discovery.

Results

We simulate RNA-seq read mapping over 9.5 M common SNPs and indels, with 15.6% of variants showing biased mapping rate for reference versus non-reference reads. However, removing potentially biased RNA-seq reads from an eQTL dataset of 185 individuals has a very small effect on gene and exon quantifications and eQTL discovery. We detect only a handful of likely false positive eQTLs, and overall eQTL SNPs show no significant enrichment for high mapping bias.

Conclusion

Our results suggest that RNA-seq quantifications are generally robust against allelic mapping bias, and that this does not have a severe effect on eQTL discovery. Nevertheless, we provide our catalog of putatively biased loci to allow better controlling for mapping bias to obtain more accurate results in future RNA-seq studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0467-2) contains supplementary material, which is available to authorized users.     

Edge effects in calling variants from targeted amplicon sequencing

Background

Analysis of targeted amplicon sequencing data presents some unique challenges in comparison to the analysis of random fragment sequencing data. Whereas reads from randomly fragmented DNA have arbitrary start positions, the reads from amplicon sequencing have fixed start positions that coincide with the amplicon boundaries. As a result, any variants near the amplicon boundaries can cause misalignments of multiple reads that can ultimately lead to false-positive or false-negative variant calls.

Results

We show that amplicon boundaries are variant calling blind spots where the variant calls are highly inaccurate. We propose that an effective strategy to avoid these blind spots is to incorporate the primer bases in obtaining read alignments and post-processing of the alignments, thereby effectively moving these blind spots into the primer binding regions (which are not used for variant calling). Targeted sequencing data analysis pipelines can provide better variant calling accuracy when primer bases are retained and sequenced.

Conclusions

Read bases beyond the variant site are necessary for analysis of amplicon sequencing data. Enzymatic primer digestion, if used in the target enrichment process, should leave at least a few primer bases to ensure that these bases are available during data analysis. The primer bases should only be removed immediately before the variant calling step to ensure that the variants can be called irrespective of where they occur within the amplicon insert region.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1073) contains supplementary material, which is available to authorized users.     

Scalable transcriptome preparation for massive parallel sequencing

Background

The tremendous output of massive parallel sequencing technologies requires automated robust and scalable sample preparation methods to fully exploit the new sequence capacity.

Methodology

In this study, a method for automated library preparation of RNA prior to massively parallel sequencing is presented. The automated protocol uses precipitation onto carboxylic acid paramagnetic beads for purification and size selection of both RNA and DNA. The automated sample preparation was compared to the standard manual sample preparation.

Conclusion/Significance

The automated procedure was used to generate libraries for gene expression profiling on the Illumina HiSeq 2000 platform with the capacity of 12 samples per preparation with a significantly improved throughput compared to the standard manual preparation. The data analysis shows consistent gene expression profiles in terms of sensitivity and quantification of gene expression between the two library preparation methods.     

CGAP-Align: A High Performance DNA Short Read Alignment Tool

Background

Next generation sequencing platforms have greatly reduced sequencing costs, leading to the production of unprecedented amounts of sequence data. BWA is one of the most popular alignment tools due to its relatively high accuracy. However, mapping reads using BWA is still the most time consuming step in sequence analysis. Increasing mapping efficiency would allow the community to better cope with ever expanding volumes of sequence data.

Results

We designed a new program, CGAP-align, that achieves a performance improvement over BWA without sacrificing recall or precision. This is accomplished through the use of Suffix Tarray, a novel data structure combining elements of Suffix Array and Suffix Tree. We also utilize a tighter lower bound estimation for the number of mismatches in a read, allowing for more effective pruning during inexact mapping. Evaluation of both simulated and real data suggests that CGAP-align consistently outperforms the current version of BWA and can achieve over twice its speed under certain conditions, all while obtaining nearly identical results.

Conclusion

CGAP-align is a new time efficient read alignment tool that extends and improves BWA. The increase in alignment speed will be of critical assistance to all sequence-based research and medicine. CGAP-align is freely available to the academic community at http://sourceforge.net/p/cgap-align under the GNU General Public License (GPL).     

Pollux: platform independent error correction of single and mixed genomes

Background

Second-generation sequencers generate millions of relatively short, but error-prone, reads. These errors make sequence assembly and other downstream projects more challenging. Correcting these errors improves the quality of assemblies and projects which benefit from error-free reads.

Results

We have developed a general-purpose error corrector that corrects errors introduced by Illumina, Ion Torrent, and Roche 454 sequencing technologies and can be applied to single- or mixed-genome data. In addition to correcting substitution errors, we locate and correct insertion, deletion, and homopolymer errors while remaining sensitive to low coverage areas of sequencing projects. Using published data sets, we correct 94% of Illumina MiSeq errors, 88% of Ion Torrent PGM errors, 85% of Roche 454 GS Junior errors. Introduced errors are 20 to 70 times more rare than successfully corrected errors. Furthermore, we show that the quality of assemblies improves when reads are corrected by our software.

Conclusions

Pollux is highly effective at correcting errors across platforms, and is consistently able to perform as well or better than currently available error correction software. Pollux provides general-purpose error correction and may be used in applications with or without assembly.     

seqCNA: an R package for DNA copy number analysis in cancer using high-throughput sequencing

Background

Deviations in the amount of genomic content that arise during tumorigenesis, called copy number alterations, are structural rearrangements that can critically affect gene expression patterns. Additionally, copy number alteration profiles allow insight into cancer discrimination, progression and complexity. On data obtained from high-throughput sequencing, improving quality through GC bias correction and keeping false positives to a minimum help build reliable copy number alteration profiles.

Results

We introduce seqCNA, a parallelized R package for an integral copy number analysis of high-throughput sequencing cancer data. The package includes novel methodology on (i) filtering, reducing false positives, and (ii) GC content correction, improving copy number profile quality, especially under great read coverage and high correlation between GC content and copy number. Adequate analysis steps are automatically chosen based on availability of paired-end mapping, matched normal samples and genome annotation.

Conclusions

seqCNA, available through Bioconductor, provides accurate copy number predictions in tumoural data, thanks to the extensive filtering and better GC bias correction, while providing an integrated and parallelized workflow.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-178) contains supplementary material, which is available to authorized users.     

Mapping Accuracy of Short Reads from Massively Parallel Sequencing and the Implications for Quantitative Expression Profiling

Background

Massively parallel sequencing offers an enormous potential for expression profiling, in particular for interspecific comparisons. Currently, different platforms for massively parallel sequencing are available, which differ in read length and sequencing costs. The 454-technology offers the highest read length. The other sequencing technologies are more cost effective, on the expense of shorter reads. Reliable expression profiling by massively parallel sequencing depends crucially on the accuracy to which the reads could be mapped to the corresponding genes.

Methodology/Principal Findings

We performed an in silico analysis to evaluate whether incorrect mapping of the sequence reads results in a biased expression pattern. A comparison of six available mapping software tools indicated a considerable heterogeneity in mapping speed and accuracy. Independently of the software used to map the reads, we found that for compact genomes both short (35 bp, 50 bp) and long sequence reads (100 bp) result in an almost unbiased expression pattern. In contrast, for species with a larger genome containing more gene families and repetitive DNA, shorter reads (35–50 bp) produced a considerable bias in gene expression. In humans, about 10% of the genes had fewer than 50% of the sequence reads correctly mapped. Sequence polymorphism up to 9% had almost no effect on the mapping accuracy of 100 bp reads. For 35 bp reads up to 3% sequence divergence did not affect the mapping accuracy strongly. The effect of indels on the mapping efficiency strongly depends on the mapping software.

Conclusions/Significance

In complex genomes, expression profiling by massively parallel sequencing could introduce a considerable bias due to incorrectly mapped sequence reads if the read length is short. Nevertheless, this bias could be accounted for if the genomic sequence is known. Furthermore, sequence polymorphisms and indels also affect the mapping accuracy and may cause a biased gene expression measurement. The choice of the mapping software is highly critical and the reliability depends on the presence/absence of indels and the divergence between reads and the reference genome. Overall, we found SSAHA2 and CLC to produce the most reliable mapping results.     

A Bead-based Normalization for Uniform Sequencing depth (BeNUS) protocol for multi-samples sequencing exemplified by HLA-B

Background

Human leukocyte antigen (HLA) is a group of genes that are extremely polymorphic among individuals and populations and have been associated with more than 100 different diseases and adverse drug effects. HLA typing is accordingly an important tool in clinical application, medical research, and population genetics. We have previously developed a phase-defined HLA gene sequencing method using MiSeq sequencing.

Results

Here we report a simple, high-throughput, and cost-effective sequencing method that includes normalized library preparation and adjustment of DNA molar concentration. We applied long-range PCR to amplify HLA-B for 96 samples followed by transposase-based library construction and multiplex sequencing with the MiSeq sequencer. After sequencing, we observed low variation in read percentages (0.2% to 1.55%) among the 96 demultiplexed samples. On this basis, all the samples were amenable to haplotype phasing using our phase-defined sequencing method. In our study, a sequencing depth of 800x was necessary and sufficient to achieve full phasing of HLA-B alleles with reliable assignment of the allelic sequence to the 8 digit level.

Conclusions

Our HLA sequencing method optimized for 96 multiplexing samples is highly time effective and cost effective and is especially suitable for automated multi-sample library preparation and sequencing.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-645) contains supplementary material, which is available to authorized users.     

SMRT sequencing only de novo assembly of the sugar beet (Beta vulgaris) chloroplast genome

Background

Third generation sequencing methods, like SMRT (Single Molecule, Real-Time) sequencing developed by Pacific Biosciences, offer much longer read length in comparison to Next Generation Sequencing (NGS) methods. Hence, they are well suited for de novo- or re-sequencing projects. Sequences generated for these purposes will not only contain reads originating from the nuclear genome, but also a significant amount of reads originating from the organelles of the target organism. These reads are usually discarded but they can also be used for an assembly of organellar replicons. The long read length supports resolution of repetitive regions and repeats within the organelles genome which might be problematic when just using short read data. Additionally, SMRT sequencing is less influenced by GC rich areas and by long stretches of the same base.

Results

We describe a workflow for a de novo assembly of the sugar beet (Beta vulgaris ssp. vulgaris) chloroplast genome sequence only based on data originating from a SMRT sequencing dataset targeted on its nuclear genome. We show that the data obtained from such an experiment are sufficient to create a high quality assembly with a higher reliability than assemblies derived from e.g. Illumina reads only. The chloroplast genome is especially challenging for de novo assembling as it contains two large inverted repeat (IR) regions. We also describe some limitations that still apply even though long reads are used for the assembly.

Conclusions

SMRT sequencing reads extracted from a dataset created for nuclear genome (re)sequencing can be used to obtain a high quality de novo assembly of the chloroplast of the sequenced organism. Even with a relatively small overall coverage for the nuclear genome it is possible to collect more than enough reads to generate a high quality assembly that outperforms short read based assemblies. However, even with long reads it is not always possible to clarify the order of elements of a chloroplast genome sequence reliantly which we could demonstrate with Fosmid End Sequences (FES) generated with Sanger technology. Nevertheless, this limitation also applies to short read sequencing data but is reached in this case at a much earlier stage during finishing.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0726-6) contains supplementary material, which is available to authorized users.     

Unlocking Short Read Sequencing for Metagenomics

Background

Different high-throughput nucleic acid sequencing platforms are currently available but a trade-off currently exists between the cost and number of reads that can be generated versus the read length that can be achieved.

Methodology/Principal Findings

We describe an experimental and computational pipeline yielding millions of reads that can exceed 200 bp with quality scores approaching that of traditional Sanger sequencing. The method combines an automatable gel-less library construction step with paired-end sequencing on a short-read instrument. With appropriately sized library inserts, mate-pair sequences can overlap, and we describe the SHERA software package that joins them to form a longer composite read.

Conclusions/Significance

This strategy is broadly applicable to sequencing applications that benefit from low-cost high-throughput sequencing, but require longer read lengths. We demonstrate that our approach enables metagenomic analyses using the Illumina Genome Analyzer, with low error rates, and at a fraction of the cost of pyrosequencing.     

NoDe: a fast error-correction algorithm for pyrosequencing amplicon reads

Background

The popularity of new sequencing technologies has led to an explosion of possible applications, including new approaches in biodiversity studies. However each of these sequencing technologies suffers from sequencing errors originating from different factors. For 16S rRNA metagenomics studies, the 454 pyrosequencing technology is one of the most frequently used platforms, but sequencing errors still lead to important data analysis issues (e.g. in clustering in taxonomic units and biodiversity estimation). Moreover, retaining a higher portion of the sequencing data by preserving as much of the read length as possible while maintaining the error rate within an acceptable range, will have important consequences at the level of taxonomic precision.

Results

The new error correction algorithm proposed in this work - NoDe (Noise Detector) - is trained to identify those positions in 454 sequencing reads that are likely to have an error, and subsequently clusters those error-prone reads with correct reads resulting in error-free representative read. A benchmarking study with other denoising algorithms shows that NoDe can detect up to 75% more errors in a large scale mock community dataset, and this with a low computational cost compared to the second best algorithm considered in this study. The positive effect of NoDe in 16S rRNA studies was confirmed by the beneficial effect on the precision of the clustering of pyrosequencing reads in operational taxonomic units.

Conclusions

NoDe was shown to be a computational efficient denoising algorithm for pyrosequencing reads, producing the lowest error rates in an extensive benchmarking study with other denoising algorithms.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0520-5) contains supplementary material, which is available to authorized users.     

Evaluation of variant identification methods for whole genome sequencing data in dairy cattle

Background

Advances in human genomics have allowed unprecedented productivity in terms of algorithms, software, and literature available for translating raw next-generation sequence data into high-quality information. The challenges of variant identification in organisms with lower quality reference genomes are less well documented. We explored the consequences of commonly recommended preparatory steps and the effects of single and multi sample variant identification methods using four publicly available software applications (Platypus, HaplotypeCaller, Samtools and UnifiedGenotyper) on whole genome sequence data of 65 key ancestors of Swiss dairy cattle populations. Accuracy of calling next-generation sequence variants was assessed by comparison to the same loci from medium and high-density single nucleotide variant (SNV) arrays.

Results

The total number of SNVs identified varied by software and method, with single (multi) sample results ranging from 17.7 to 22.0 (16.9 to 22.0) million variants. Computing time varied considerably between software. Preparatory realignment of insertions and deletions and subsequent base quality score recalibration had only minor effects on the number and quality of SNVs identified by different software, but increased computing time considerably. Average concordance for single (multi) sample results with high-density chip data was 58.3% (87.0%) and average genotype concordance in correctly identified SNVs was 99.2% (99.2%) across software. The average quality of SNVs identified, measured as the ratio of transitions to transversions, was higher using single sample methods than multi sample methods. A consensus approach using results of different software generally provided the highest variant quality in terms of transition/transversion ratio.

Conclusions

Our findings serve as a reference for variant identification pipeline development in non-human organisms and help assess the implication of preparatory steps in next-generation sequencing pipelines for organisms with incomplete reference genomes (pipeline code is included). Benchmarking this information should prove particularly useful in processing next-generation sequencing data for use in genome-wide association studies and genomic selection.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-948) contains supplementary material, which is available to authorized users.     

Reference-Free Validation of Short Read Data

Background

High-throughput DNA sequencing techniques offer the ability to rapidly and cheaply sequence material such as whole genomes. However, the short-read data produced by these techniques can be biased or compromised at several stages in the sequencing process; the sources and properties of some of these biases are not always known. Accurate assessment of bias is required for experimental quality control, genome assembly, and interpretation of coverage results. An additional challenge is that, for new genomes or material from an unidentified source, there may be no reference available against which the reads can be checked.

Results

We propose analytical methods for identifying biases in a collection of short reads, without recourse to a reference. These, in conjunction with existing approaches, comprise a methodology that can be used to quantify the quality of a set of reads. Our methods involve use of three different measures: analysis of base calls; analysis of k-mers; and analysis of distributions of k-mers. We apply our methodology to wide range of short read data and show that, surprisingly, strong biases appear to be present. These include gross overrepresentation of some poly-base sequences, per-position biases towards some bases, and apparent preferences for some starting positions over others.

Conclusions

The existence of biases in short read data is known, but they appear to be greater and more diverse than identified in previous literature. Statistical analysis of a set of short reads can help identify issues prior to assembly or resequencing, and should help guide chemical or statistical methods for bias rectification.     

Transcriptome-based differentiation of closely-related Miscanthus lines

Background

Distinguishing between individuals is critical to those conducting animal/plant breeding, food safety/quality research, diagnostic and clinical testing, and evolutionary biology studies. Classical genetic identification studies are based on marker polymorphisms, but polymorphism-based techniques are time and labor intensive and often cannot distinguish between closely related individuals. Illumina sequencing technologies provide the detailed sequence data required for rapid and efficient differentiation of related species, lines/cultivars, and individuals in a cost-effective manner. Here we describe the use of Illumina high-throughput exome sequencing, coupled with SNP mapping, as a rapid means of distinguishing between related cultivars of the lignocellulosic bioenergy crop giant miscanthus (Miscanthus × giganteus). We provide the first exome sequence database for Miscanthus species complete with Gene Ontology (GO) functional annotations.

Results

A SNP comparative analysis of rhizome-derived cDNA sequences was successfully utilized to distinguish three Miscanthus × giganteus cultivars from each other and from other Miscanthus species. Moreover, the resulting phylogenetic tree generated from SNP frequency data parallels the known breeding history of the plants examined. Some of the giant miscanthus plants exhibit considerable sequence divergence.

Conclusions

Here we describe an analysis of Miscanthus in which high-throughput exome sequencing was utilized to differentiate between closely related genotypes despite the current lack of a reference genome sequence. We functionally annotated the exome sequences and provide resources to support Miscanthus systems biology. In addition, we demonstrate the use of the commercial high-performance cloud computing to do computational GO annotation.