Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source .     

四种常用的生物序列比对软件比较

随着高通量测序技术的快速发展,下一代测序技术也迅速发展为生物领域中的主流技术,而理解下一代测序数据最重要的一步是比对。比对是进行后续生物信息分析的基石,也因此催生了很多比对软件。本文主要选取了四种常用的比对软件Bowtie2、BWA、MAQ和SOAP2,对这四种软件及算法进行综述,并通过实际测序数据对四种软件进行比较和评估,为生物学研究者选择最佳的短序列比对软件提供理论和实践依据。     

Coverage-based consensus calling (CbCC) of short sequence reads and comparison of CbCC results to identify SNPs in chickpea (Cicer arietinum; Fabaceae), a crop species without a reference genome

? Premise of the study: Next-generation sequencing (NGS) technologies are frequently used for resequencing and mining of single nucleotide polymorphisms (SNPs) by comparison to a reference genome. In crop species such as chickpea (Cicer arietinum) that lack a reference genome sequence, NGS-based SNP discovery is a challenge. Therefore, unlike probability-based statistical approaches for consensus calling and by comparison with a reference sequence, a coverage-based consensus calling (CbCC) approach was applied and two genotypes were compared for SNP identification. ? Methods: A CbCC approach is used in this study with four commonly used short read alignment tools (Maq, Bowtie, Novoalign, and SOAP2) and 15.7 and 22.1 million Illumina reads for chickpea genotypes ICC4958 and ICC1882, together with the chickpea trancriptome assembly (CaTA). ? Key results: A nonredundant set of 4543 SNPs was identified between two chickpea genotypes. Experimental validation of 224 randomly selected SNPs showed superiority of Maq among individual tools, as 50.0% of SNPs predicted by Maq were true SNPs. For combinations of two tools, greatest accuracy (55.7%) was reported for Maq and Bowtie, with a combination of Bowtie, Maq, and Novoalign identifying 61.5% true SNPs. SNP prediction accuracy generally increased with increasing reads depth. ? Conclusions: This study provides a benchmark comparison of tools as well as read depths for four commonly used tools for NGS SNP discovery in a crop species without a reference genome sequence. In addition, a large number of SNPs have been identified in chickpea that would be useful for molecular breeding.     

Comparative study of sequence aligners for detecting antibiotic resistance in bacterial metagenomes

We aim to compare the performance of Bowtie2 , bwa‐mem , blastn and blastx when aligning bacterial metagenomes against the Comprehensive Antibiotic Resistance Database (CARD). Simulated reads were used to evaluate the performance of each aligner under the following four performance criteria: correctly mapped, false positives, multi‐reads and partials. The optimal alignment approach was applied to samples from two wastewater treatment plants to detect antibiotic resistance genes using next generation sequencing. blastn mapped with greater accuracy among the four sequence alignment approaches considered followed by Bowtie2 . blastx generated the greatest number of false positives and multi‐reads when aligned against the CARD. The performance of each alignment tool was also investigated using error‐free reads. Although each aligner mapped a greater number of error‐free reads as compared to Illumina‐error reads, in general, the introduction of sequencing errors had little effect on alignment results when aligning against the CARD. Given each performance criteria, blastn was found to be the most favourable alignment tool and was therefore used to assess resistance genes in sewage samples. Beta‐lactam and aminoglycoside were found to be the most abundant classes of antibiotic resistance genes in each sample.

Significance and Impact of the Study

Antibiotic resistance genes (ARGs) are pollutants known to persist in wastewater treatment plants among other environments, thus methods for detecting these genes have become increasingly relevant. Next generation sequencing has brought about a host of sequence alignment tools that provide a comprehensive look into antimicrobial resistance in environmental samples. However, standardizing practices in ARG metagenomic studies is challenging since results produced from alignment tools can vary significantly. Our study provides sequence alignment results of synthetic, and authentic bacterial metagenomes mapped against an ARG database using multiple alignment tools, and the best practice for detecting ARGs in environmental samples.     

Searching for SNPs with cloud computing

As DNA sequencing outpaces improvements in computer speed, there is a critical need to accelerate tasks like alignment and SNP calling. Crossbow is a cloud-computing software tool that combines the aligner Bowtie and the SNP caller SOAPsnp. Executing in parallel using Hadoop, Crossbow analyzes data comprising 38-fold coverage of the human genome in three hours using a 320-CPU cluster rented from a cloud computing service for about $85. Crossbow is available from .     

Fast gapped-read alignment with Bowtie 2

As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.     

Performance of microarray and liquid based capture methods for target enrichment for massively parallel sequencing and SNP discovery

Targeted sequencing is a cost-efficient way to obtain answers to biological questions in many projects, but the choice of the enrichment method to use can be difficult. In this study we compared two hybridization methods for target enrichment for massively parallel sequencing and single nucleotide polymorphism (SNP) discovery, namely Nimblegen sequence capture arrays and the SureSelect liquid-based hybrid capture system. We prepared sequencing libraries from three HapMap samples using both methods, sequenced the libraries on the Illumina Genome Analyzer, mapped the sequencing reads back to the genome, and called variants in the sequences. 74-75% of the sequence reads originated from the targeted region in the SureSelect libraries and 41-67% in the Nimblegen libraries. We could sequence up to 99.9% and 99.5% of the regions targeted by capture probes from the SureSelect libraries and from the Nimblegen libraries, respectively. The Nimblegen probes covered 0.6 Mb more of the original 3.1 Mb target region than the SureSelect probes. In each sample, we called more SNPs and detected more novel SNPs from the libraries that were prepared using the Nimblegen method. Thus the Nimblegen method gave better results when judged by the number of SNPs called, but this came at the cost of more over-sampling.     

Performance evaluation method for read mapping tool in clinical panel sequencing

In addition to the rapid advancement in Next-Generation Sequencing (NGS) technology, clinical panel sequencing is being used increasingly in clinical studies and tests. However, tools that are used in NGS data analysis have not been comparatively evaluated in performance for panel sequencing. This study aimed to evaluate the tools used in the alignment process, the first procedure in bioinformatics analysis, by comparing tools that have been widely used with ones that have been introduced recently. With the accumulated panel sequencing data, detected variant lists were cataloged and inserted into simulated reads produced from the reference genome (h19). The amount of unmapped reads and misaligned reads, mapping quality distribution, and runtime were measured as standards for comparison. As the most widely used tools, Bowtie2 and BWA–MEM each showed explicit performance with AUC of 0.9984 and 0.9970 respectively. Kart, maintaining superior runtime and less number of misaligned read, also similarly possessed high level of AUC (0.9723). Such selection and optimization method of tools appropriate for panel sequencing can be utilized for fields requiring error minimization, such as clinical application and liquid biopsy studies.     

A Robust Protocol to Increase NimbleGen SeqCap EZ Multiplexing Capacity to 96 Samples

Contemporary genetic studies frequently involve sequencing of a targeted gene panel, for instance consisting of a set of genes associated with a specific disease. The NimbleGen SeqCap EZ Choice kit is commonly used for the targeted enrichment of sequencing libraries comprising a target size up to 7 Mb. A major drawback of this commercially available method is the exclusive use of single-indexing, meaning that at most 24 samples can be multiplexed in a single reaction. In case of relatively small target sizes, this will lead to excessive amounts of data per sample. We present an extended version of the NimbleGen SeqCap EZ protocol which allows to robustly multiplex up to 96 samples. We achieved this by incorporating Illumina dual-indexing based custom adapters into the original protocol. To further extend the optimization of cost-efficient sequencing of custom target panels, we studied the effect of higher pre-enrichment pooling factors and show that pre-enrichment pooling of up to 12 samples does not affect the quality of the data. To facilitate evaluation of capture efficiency in custom design panels, we also provide a detailed reporting tool.     

A beginners guide to SNP calling from high-throughput DNA-sequencing data

High-throughput DNA sequencing (HTS) is of increasing importance in the life sciences. One of its most prominent applications is the sequencing of whole genomes or targeted regions of the genome such as all exonic regions (i.e., the exome). Here, the objective is the identification of genetic variants such as single nucleotide polymorphisms (SNPs). The extraction of SNPs from the raw genetic sequences involves many processing steps and the application of a diverse set of tools. We review the essential building blocks for a pipeline that calls SNPs from raw HTS data. The pipeline includes quality control, mapping of short reads to the reference genome, visualization and post-processing of the alignment including base quality recalibration. The final steps of the pipeline include the SNP calling procedure along with filtering of SNP candidates. The steps of this pipeline are accompanied by an analysis of a publicly available whole-exome sequencing dataset. To this end, we employ several alignment programs and SNP calling routines for highlighting the fact that the choice of the tools significantly affects the final results.     

Microfluidic droplet enrichment for targeted sequencing

Targeted sequence enrichment enables better identification of genetic variation by providing increased sequencing coverage for genomic regions of interest. Here, we report the development of a new target enrichment technology that is highly differentiated from other approaches currently in use. Our method, MESA (Microfluidic droplet Enrichment for Sequence Analysis), isolates genomic DNA fragments in microfluidic droplets and performs TaqMan PCR reactions to identify droplets containing a desired target sequence. The TaqMan positive droplets are subsequently recovered via dielectrophoretic sorting, and the TaqMan amplicons are removed enzymatically prior to sequencing. We demonstrated the utility of this approach by generating an average 31.6-fold sequence enrichment across 250 kb of targeted genomic DNA from five unique genomic loci. Significantly, this enrichment enabled a more comprehensive identification of genetic polymorphisms within the targeted loci. MESA requires low amounts of input DNA, minimal prior locus sequence information and enriches the target region without PCR bias or artifacts. These features make it well suited for the study of genetic variation in a number of research and diagnostic applications.     

Fast and Sensitive Alignment of Microbial Whole Genome Sequencing Reads to Large Sequence Datasets on a Desktop PC: Application to Metagenomic Datasets and Pathogen Identification

Next generation sequencing (NGS) of metagenomic samples is becoming a standard approach to detect individual species or pathogenic strains of microorganisms. Computer programs used in the NGS community have to balance between speed and sensitivity and as a result, species or strain level identification is often inaccurate and low abundance pathogens can sometimes be missed. We have developed Taxoner, an open source, taxon assignment pipeline that includes a fast aligner (e.g. Bowtie2) and a comprehensive DNA sequence database. We tested the program on simulated datasets as well as experimental data from Illumina, IonTorrent, and Roche 454 sequencing platforms. We found that Taxoner performs as well as, and often better than BLAST, but requires two orders of magnitude less running time meaning that it can be run on desktop or laptop computers. Taxoner is slower than the approaches that use small marker databases but is more sensitive due the comprehensive reference database. In addition, it can be easily tuned to specific applications using small tailored databases. When applied to metagenomic datasets, Taxoner can provide a functional summary of the genes mapped and can provide strain level identification. Taxoner is written in C for Linux operating systems. The code and documentation are available for research applications at http://code.google.com/p/taxoner.     

MutAid: Sanger and NGS Based Integrated Pipeline for Mutation Identification,Validation and Annotation in Human Molecular Genetics

Traditional Sanger sequencing as well as Next-Generation Sequencing have been used for the identification of disease causing mutations in human molecular research. The majority of currently available tools are developed for research and explorative purposes and often do not provide a complete, efficient, one-stop solution. As the focus of currently developed tools is mainly on NGS data analysis, no integrative solution for the analysis of Sanger data is provided and consequently a one-stop solution to analyze reads from both sequencing platforms is not available. We have therefore developed a new pipeline called MutAid to analyze and interpret raw sequencing data produced by Sanger or several NGS sequencing platforms. It performs format conversion, base calling, quality trimming, filtering, read mapping, variant calling, variant annotation and analysis of Sanger and NGS data under a single platform. It is capable of analyzing reads from multiple patients in a single run to create a list of potential disease causing base substitutions as well as insertions and deletions. MutAid has been developed for expert and non-expert users and supports four sequencing platforms including Sanger, Illumina, 454 and Ion Torrent. Furthermore, for NGS data analysis, five read mappers including BWA, TMAP, Bowtie, Bowtie2 and GSNAP and four variant callers including GATK-HaplotypeCaller, SAMTOOLS, Freebayes and VarScan2 pipelines are supported. MutAid is freely available at https://sourceforge.net/projects/mutaid.     

MiST: A new approach to variant detection in deep sequencing datasets

MiST is a novel approach to variant calling from deep sequencing data, using the inverted mapping approach developed for Geoseq. Reads that can map to a targeted exonic region are identified using exact matches to tiles from the region. The reads are then aligned to the targets to discover variants. MiST carefully handles paralogous reads that map ambiguously to the genome and clonal reads arising from PCR bias, which are the two major sources of errors in variant calling. The reduced computational complexity of mapping selected reads to targeted regions of the genome improves speed, specificity and sensitivity of variant detection. Compared with variant calls from the GATK platform, MiST showed better concordance with SNPs from dbSNP and genotypes determined by an exonic-SNP array. Variant calls made only by MiST confirm at a high rate (>90%) by Sanger sequencing. Thus, MiST is a valuable alternative tool to analyse variants in deep sequencing data.     

Target region selection is a critical determinant of community fingerprints generated by 16S pyrosequencing

Pyrosequencing of 16S rRNA genes allows for in-depth characterization of complex microbial communities. Although it is known that primer selection can influence the profile of a community generated by sequencing, the extent and severity of this bias on deep-sequencing methodologies is not well elucidated. We tested the hypothesis that the hypervariable region targeted for sequencing and primer degeneracy play important roles in influencing the composition of 16S pyrotag communities. Subgingival plaque from deep sites of current smokers with chronic periodontitis was analyzed using Sanger sequencing and pyrosequencing using 4 primer pairs. Greater numbers of species were detected by pyrosequencing than by Sanger sequencing. Rare taxa constituted nearly 6% of each pyrotag community and less than 1% of the Sanger sequencing community. However, the different target regions selected for pyrosequencing did not demonstrate a significant difference in the number of rare and abundant taxa detected. The genera Prevotella, Fusobacterium, Streptococcus, Granulicatella, Bacteroides, Porphyromonas and Treponema were abundant when the V1-V3 region was targeted, while Streptococcus, Treponema, Prevotella, Eubacterium, Porphyromonas, Campylobacter and Enterococcus predominated in the community generated by V4-V6 primers, and the most numerous genera in the V7-V9 community were Veillonella, Streptococcus, Eubacterium, Enterococcus, Treponema, Catonella and Selenomonas. Targeting the V4-V6 region failed to detect the genus Fusobacterium, while the taxa Selenomonas, TM7 and Mycoplasma were not detected by the V7-V9 primer pairs. The communities generated by degenerate and non-degenerate primers did not demonstrate significant differences. Averaging the community fingerprints generated by V1-V3 and V7-V9 primers provided results similar to Sanger sequencing, while allowing a significantly greater depth of coverage than is possible with Sanger sequencing. It is therefore important to use primers targeted to these two regions of the 16S rRNA gene in all deep-sequencing efforts to obtain representational characterization of complex microbial communities.     

FANSe2: A Robust and Cost-Efficient Alignment Tool for Quantitative Next-Generation Sequencing Applications

Correct and bias-free interpretation of the deep sequencing data is inevitably dependent on the complete mapping of all mappable reads to the reference sequence, especially for quantitative RNA-seq applications. Seed-based algorithms are generally slow but robust, while Burrows-Wheeler Transform (BWT) based algorithms are fast but less robust. To have both advantages, we developed an algorithm FANSe2 with iterative mapping strategy based on the statistics of real-world sequencing error distribution to substantially accelerate the mapping without compromising the accuracy. Its sensitivity and accuracy are higher than the BWT-based algorithms in the tests using both prokaryotic and eukaryotic sequencing datasets. The gene identification results of FANSe2 is experimentally validated, while the previous algorithms have false positives and false negatives. FANSe2 showed remarkably better consistency to the microarray than most other algorithms in terms of gene expression quantifications. We implemented a scalable and almost maintenance-free parallelization method that can utilize the computational power of multiple office computers, a novel feature not present in any other mainstream algorithm. With three normal office computers, we demonstrated that FANSe2 mapped an RNA-seq dataset generated from an entire Illunima HiSeq 2000 flowcell (8 lanes, 608 M reads) to masked human genome within 4.1 hours with higher sensitivity than Bowtie/Bowtie2. FANSe2 thus provides robust accuracy, full indel sensitivity, fast speed, versatile compatibility and economical computational utilization, making it a useful and practical tool for deep sequencing applications. FANSe2 is freely available at http://bioinformatics.jnu.edu.cn/software/fanse2/.     

Detection of Genomic Variation by Selection of a 9 Mb DNA Region and High Throughput Sequencing

Detection of the rare polymorphisms and causative mutations of genetic diseases in a targeted genomic area has become a major goal in order to understand genomic and phenotypic variability. We have interrogated repeat-masked regions of 8.9 Mb on human chromosomes 21 (7.8 Mb) and 7 (1.1 Mb) from an individual from the International HapMap Project (NA12872). We have optimized a method of genomic selection for high throughput sequencing. Microarray-based selection and sequencing resulted in 260-fold enrichment, with 41% of reads mapping to the target region. 83% of SNPs in the targeted region had at least 4-fold sequence coverage and 54% at least 15-fold. When assaying HapMap SNPs in NA12872, our sequence genotypes are 91.3% concordant in regions with coverage≥4-fold, and 97.9% concordant in regions with coverage≥15-fold. About 81% of the SNPs recovered with both thresholds are listed in dbSNP. We observed that regions with low sequence coverage occur in close proximity to low-complexity DNA. Validation experiments using Sanger sequencing were performed for 46 SNPs with 15-20 fold coverage, with a confirmation rate of 96%, suggesting that DNA selection provides an accurate and cost-effective method for identifying rare genomic variants.