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/.     

RAMICS: trainable,high-speed and biologically relevant alignment of high-throughput sequencing reads to coding DNA

The challenge presented by high-throughput sequencing necessitates the development of novel tools for accurate alignment of reads to reference sequences. Current approaches focus on using heuristics to map reads quickly to large genomes, rather than generating highly accurate alignments in coding regions. Such approaches are, thus, unsuited for applications such as amplicon-based analysis and the realignment phase of exome sequencing and RNA-seq, where accurate and biologically relevant alignment of coding regions is critical. To facilitate such analyses, we have developed a novel tool, RAMICS, that is tailored to mapping large numbers of sequence reads to short lengths (<10 000 bp) of coding DNA. RAMICS utilizes profile hidden Markov models to discover the open reading frame of each sequence and aligns to the reference sequence in a biologically relevant manner, distinguishing between genuine codon-sized indels and frameshift mutations. This approach facilitates the generation of highly accurate alignments, accounting for the error biases of the sequencing machine used to generate reads, particularly at homopolymer regions. Performance improvements are gained through the use of graphics processing units, which increase the speed of mapping through parallelization. RAMICS substantially outperforms all other mapping approaches tested in terms of alignment quality while maintaining highly competitive speed performance.     

From Gigabyte to Kilobyte: A Bioinformatics Protocol for Mining Large RNA-Seq Transcriptomics Data

RNA-Seq techniques generate hundreds of millions of short RNA reads using next-generation sequencing (NGS). These RNA reads can be mapped to reference genomes to investigate changes of gene expression but improved procedures for mining large RNA-Seq datasets to extract valuable biological knowledge are needed. RNAMiner—a multi-level bioinformatics protocol and pipeline—has been developed for such datasets. It includes five steps: Mapping RNA-Seq reads to a reference genome, calculating gene expression values, identifying differentially expressed genes, predicting gene functions, and constructing gene regulatory networks. To demonstrate its utility, we applied RNAMiner to datasets generated from Human, Mouse, Arabidopsis thaliana, and Drosophila melanogaster cells, and successfully identified differentially expressed genes, clustered them into cohesive functional groups, and constructed novel gene regulatory networks. The RNAMiner web service is available at http://calla.rnet.missouri.edu/rnaminer/index.html.     

MetaCluster 4.0: a novel binning algorithm for NGS reads and huge number of species

Next-generation sequencing (NGS) technologies allow the sequencing of microbial communities directly from the environment without prior culturing. The output of environmental DNA sequencing consists of many reads from genomes of different unknown species, making the clustering together reads from the same (or similar) species (also known as binning) a crucial step. The difficulties of the binning problem are due to the following four factors: (1) the lack of reference genomes; (2) uneven abundance ratio of species; (3) short NGS reads; and (4) a large number of species (can be more than a hundred). None of the existing binning tools can handle all four factors. No tools, including both AbundanceBin and MetaCluster 3.0, have demonstrated reasonable performance on a sample with more than 20 species. In this article, we introduce MetaCluster 4.0, an unsupervised binning algorithm that can accurately (with about 80% precision and sensitivity in all cases and at least 90% in some cases) and efficiently bin short reads with varying abundance ratios and is able to handle datasets with 100 species. The novelty of MetaCluster 4.0 stems from solving a few important problems: how to divide reads into groups by a probabilistic approach, how to estimate the 4-mer distribution of each group, how to estimate the number of species, and how to modify MetaCluster 3.0 to handle a large number of species. We show that Meta Cluster 4.0 is effective for both simulated and real datasets. Supplementary Material is available at www.liebertonline.com/cmb.     

MetaGeniE: Characterizing Human Clinical Samples Using Deep Metagenomic Sequencing

With the decreasing cost of next-generation sequencing, deep sequencing of clinical samples provides unique opportunities to understand host-associated microbial communities. Among the primary challenges of clinical metagenomic sequencing is the rapid filtering of human reads to survey for pathogens with high specificity and sensitivity. Metagenomes are inherently variable due to different microbes in the samples and their relative abundance, the size and architecture of genomes, and factors such as target DNA amounts in tissue samples (i.e. human DNA versus pathogen DNA concentration). This variation in metagenomes typically manifests in sequencing datasets as low pathogen abundance, a high number of host reads, and the presence of close relatives and complex microbial communities. In addition to these challenges posed by the composition of metagenomes, high numbers of reads generated from high-throughput deep sequencing pose immense computational challenges. Accurate identification of pathogens is confounded by individual reads mapping to multiple different reference genomes due to gene similarity in different taxa present in the community or close relatives in the reference database. Available global and local sequence aligners also vary in sensitivity, specificity, and speed of detection. The efficiency of detection of pathogens in clinical samples is largely dependent on the desired taxonomic resolution of the organisms. We have developed an efficient strategy that identifies “all against all” relationships between sequencing reads and reference genomes. Our approach allows for scaling to large reference databases and then genome reconstruction by aggregating global and local alignments, thus allowing genetic characterization of pathogens at higher taxonomic resolution. These results were consistent with strain level SNP genotyping and bacterial identification from laboratory culture.     

Extending assembly of short DNA sequences to handle error

Inexpensive de novo genome sequencing, particularly in organisms with small genomes, is now possible using several new sequencing technologies. Some of these technologies such as that from Illumina's Solexa Sequencing, produce high genomic coverage by generating a very large number of small reads ( approximately 30 bp). While prior work shows that partial assembly can be performed by k-mer extension in error-free reads, this algorithm is unsuccessful with the sequencing error rates found in practice. We present VCAKE (Verified Consensus Assembly by K-mer Extension), a modification of simple k-mer extension that overcomes error by using high depth coverage. Though it is a simple modification of a previous approach, we show significant improvements in assembly results on simulated and experimental datasets that include error. AVAILABILITY: http://152.2.15.114/~labweb/VCAKE     

Whole-genome sequencing and variant discovery in C. elegans

Massively parallel sequencing instruments enable rapid and inexpensive DNA sequence data production. Because these instruments are new, their data require characterization with respect to accuracy and utility. To address this, we sequenced a Caernohabditis elegans N2 Bristol strain isolate using the Solexa Sequence Analyzer, and compared the reads to the reference genome to characterize the data and to evaluate coverage and representation. Massively parallel sequencing facilitates strain-to-reference comparison for genome-wide sequence variant discovery. Owing to the short-read-length sequences produced, we developed a revised approach to determine the regions of the genome to which short reads could be uniquely mapped. We then aligned Solexa reads from C. elegans strain CB4858 to the reference, and screened for single-nucleotide polymorphisms (SNPs) and small indels. This study demonstrates the utility of massively parallel short read sequencing for whole genome resequencing and for accurate discovery of genome-wide polymorphisms.     

FR-HIT, a very fast program to recruit metagenomic reads to homologous reference genomes

SUMMARY: Fragment recruitment, a process of aligning sequencing reads to reference genomes, is a crucial step in metagenomic data analysis. The available sequence alignment programs are either slow or insufficient for recruiting metagenomic reads. We implemented an efficient algorithm, FR-HIT, for fragment recruitment. We applied FR-HIT and several other tools including BLASTN, MegaBLAST, BLAT, LAST, SSAHA2, SOAP2, BWA and BWA-SW to recruit four metagenomic datasets from different type of sequencers. On average, FR-HIT and BLASTN recruited significantly more reads than other programs, while FR-HIT is about two orders of magnitude faster than BLASTN. FR-HIT is slower than the fastest SOAP2, BWA and BWA-SW, but it recruited 1-5 times more reads. AVAILABILITY: http://weizhongli-lab.org/frhit.     

Segtor: rapid annotation of genomic coordinates and single nucleotide variations using segment trees

Various research projects often involve determining the relative position of genomic coordinates, intervals, single nucleotide variations (SNVs), insertions, deletions and translocations with respect to genes and their potential impact on protein translation. Due to the tremendous increase in throughput brought by the use of next-generation sequencing, investigators are routinely faced with the need to annotate very large datasets. We present Segtor, a tool to annotate large sets of genomic coordinates, intervals, SNVs, indels and translocations. Our tool uses segment trees built using the start and end coordinates of the genomic features the user wishes to use instead of storing them in a database management system. The software also produces annotation statistics to allow users to visualize how many coordinates were found within various portions of genes. Our system currently can be made to work with any species available on the UCSC Genome Browser. Segtor is a suitable tool for groups, especially those with limited access to programmers or with interest to analyze large amounts of individual genomes, who wish to determine the relative position of very large sets of mapped reads and subsequently annotate observed mutations between the reads and the reference. Segtor (http://lbbc.inca.gov.br/segtor/) is an open-source tool that can be freely downloaded for non-profit use. We also provide a web interface for testing purposes.     

Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays

C57BL/6J (B6) and DBA/2J (D2) are two of the most commonly used inbred mouse strains in neuroscience research. However, the only currently available mouse genome is based entirely on the B6 strain sequence. Subsequently, oligonucleotide microarray probes are based solely on this B6 reference sequence, making their application for gene expression profiling comparisons across mouse strains dubious due to their allelic sequence differences, including single nucleotide polymorphisms (SNPs). The emergence of next-generation sequencing (NGS) and the RNA-Seq application provides a clear alternative to oligonucleotide arrays for detecting differential gene expression without the problems inherent to hybridization-based technologies. Using RNA-Seq, an average of 22 million short sequencing reads were generated per sample for 21 samples (10 B6 and 11 D2), and these reads were aligned to the mouse reference genome, allowing 16,183 Ensembl genes to be queried in striatum for both strains. To determine differential expression, 'digital mRNA counting' is applied based on reads that map to exons. The current study compares RNA-Seq (Illumina GA IIx) with two microarray platforms (Illumina MouseRef-8 v2.0 and Affymetrix MOE 430 2.0) to detect differential striatal gene expression between the B6 and D2 inbred mouse strains. We show that by using stringent data processing requirements differential expression as determined by RNA-Seq is concordant with both the Affymetrix and Illumina platforms in more instances than it is concordant with only a single platform, and that instances of discordance with respect to direction of fold change were rare. Finally, we show that additional information is gained from RNA-Seq compared to hybridization-based techniques as RNA-Seq detects more genes than either microarray platform. The majority of genes differentially expressed in RNA-Seq were only detected as present in RNA-Seq, which is important for studies with smaller effect sizes where the sensitivity of hybridization-based techniques could bias interpretation.     

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.     

Btrim: a fast, lightweight adapter and quality trimming program for next-generation sequencing technologies

Btrim is a fast and lightweight software to trim adapters and low quality regions in reads from ultra high-throughput next-generation sequencing machines. It also can reliably identify barcodes and assign the reads to the original samples. Based on a modified Myers's bit-vector dynamic programming algorithm, Btrim can handle indels in adapters and barcodes. It removes low quality regions and trims off adapters at both or either end of the reads. A typical trimming of 30 M reads with two sets of adapter pairs can be done in about a minute with a small memory footprint. Btrim is a versatile stand-alone tool that can be used as the first step in virtually all next-generation sequence analysis pipelines. The program is available at http://graphics.med.yale.edu/trim/.     

The advantages of SMRT sequencing

Of the current next-generation sequencing technologies, SMRT sequencing is sometimes overlooked. However, attributes such as long reads, modified base detection and high accuracy make SMRT a useful technology and an ideal approach to the complete sequencing of small genomes.Pacific Biosciences'' single molecule, real-time sequencing technology, SMRT, is one of several next-generation sequencing technologies that are currently in use. In the past, it has been somewhat overlooked because of its lower throughput compared with methods such as Illumina and Ion Torrent, and because of persistent rumors that it is inaccurate. Here, we seek to dispel these misconceptions and show that SMRT is indeed a highly accurate method with many advantages when used to sequence small genomes, including the possibility of facile closure of bacterial genomes without additional experimentation. We also highlight its value in being able to detect modified bases in DNA.     

The advantages of SMRT sequencing

Of the current next-generation sequencing technologies, SMRT sequencing is sometimes overlooked. However, attributes such as long reads, modified base detection and high accuracy make SMRT a useful technology and an ideal approach to the complete sequencing of small genomes.Pacific Biosciences'' single molecule, real-time sequencing technology, SMRT, is one of several next-generation sequencing technologies that are currently in use. In the past, it has been somewhat overlooked because of its lower throughput compared with methods such as Illumina and Ion Torrent, and because of persistent rumors that it is inaccurate. Here, we seek to dispel these misconceptions and show that SMRT is indeed a highly accurate method with many advantages when used to sequence small genomes, including the possibility of facile closure of bacterial genomes without additional experimentation. We also highlight its value in being able to detect modified bases in DNA.     

Genome characterization of the selected long- and short-sleep mouse lines

The Inbred Long- and Short-Sleep (ILS, ISS) mouse lines were selected for differences in acute ethanol sensitivity using the loss of righting response (LORR) as the selection trait. The lines show an over tenfold difference in LORR and, along with a recombinant inbred panel derived from them (the LXS), have been widely used to dissect the genetic underpinnings of acute ethanol sensitivity. Here we have sequenced the genomes of the ILS and ISS to investigate the DNA variants that contribute to their sensitivity difference. We identified ~2.7 million high-confidence SNPs and small indels and ~7000 structural variants between the lines; variants were found to occur in 6382 annotated genes. Using a hidden Markov model, we were able to reconstruct the genome-wide ancestry patterns of the eight inbred progenitor strains from which the ILS and ISS were derived, and found that quantitative trait loci that have been mapped for LORR were slightly enriched for DNA variants. Finally, by mapping and quantifying RNA-seq reads from the ILS and ISS to their strain-specific genomes rather than to the reference genome, we found a substantial improvement in a differential expression analysis between the lines. This work will help in identifying and characterizing the DNA sequence variants that contribute to the difference in ethanol sensitivity between the ILS and ISS and will also aid in accurate quantification of RNA-seq data generated from the LXS RIs.