Coval: Improving Alignment Quality and Variant Calling Accuracy for Next-Generation Sequencing Data

Accurate identification of DNA polymorphisms using next-generation sequencing technology is challenging because of a high rate of sequencing error and incorrect mapping of reads to reference genomes. Currently available short read aligners and DNA variant callers suffer from these problems. We developed the Coval software to improve the quality of short read alignments. Coval is designed to minimize the incidence of spurious alignment of short reads, by filtering mismatched reads that remained in alignments after local realignment and error correction of mismatched reads. The error correction is executed based on the base quality and allele frequency at the non-reference positions for an individual or pooled sample. We demonstrated the utility of Coval by applying it to simulated genomes and experimentally obtained short-read data of rice, nematode, and mouse. Moreover, we found an unexpectedly large number of incorrectly mapped reads in ‘targeted’ alignments, where the whole genome sequencing reads had been aligned to a local genomic segment, and showed that Coval effectively eliminated such spurious alignments. We conclude that Coval significantly improves the quality of short-read sequence alignments, thereby increasing the calling accuracy of currently available tools for SNP and indel identification. Coval is available at http://sourceforge.net/projects/coval105/.     

Flexible network reconstruction from relational databases with Cytoscape and CytoSQL

Background

Bisulfite sequencing using next generation sequencers yields genome-wide measurements of DNA methylation at single nucleotide resolution. Traditional aligners are not designed for mapping bisulfite-treated reads, where the unmethylated Cs are converted to Ts. We have developed BS Seeker, an approach that converts the genome to a three-letter alphabet and uses Bowtie to align bisulfite-treated reads to a reference genome. It uses sequence tags to reduce mapping ambiguity. Post-processing of the alignments removes non-unique and low-quality mappings.

Results

We tested our aligner on synthetic data, a bisulfite-converted Arabidopsis library, and human libraries generated from two different experimental protocols. We evaluated the performance of our approach and compared it to other bisulfite aligners. The results demonstrate that among the aligners tested, BS Seeker is more versatile and faster. When mapping to the human genome, BS Seeker generates alignments significantly faster than RMAP and BSMAP. Furthermore, BS Seeker is the only alignment tool that can explicitly account for tags which are generated by certain library construction protocols.

Conclusions

BS Seeker provides fast and accurate mapping of bisulfite-converted reads. It can work with BS reads generated from the two different experimental protocols, and is able to efficiently map reads to large mammalian genomes. The Python program is freely available at http://pellegrini.mcdb.ucla.edu/BS_Seeker/BS_Seeker.html.     

NINJA-OPS: Fast Accurate Marker Gene Alignment Using Concatenated Ribosomes

The explosion of bioinformatics technologies in the form of next generation sequencing (NGS) has facilitated a massive influx of genomics data in the form of short reads. Short read mapping is therefore a fundamental component of next generation sequencing pipelines which routinely match these short reads against reference genomes for contig assembly. However, such techniques have seldom been applied to microbial marker gene sequencing studies, which have mostly relied on novel heuristic approaches. We propose NINJA Is Not Just Another OTU-Picking Solution (NINJA-OPS, or NINJA for short), a fast and highly accurate novel method enabling reference-based marker gene matching (picking Operational Taxonomic Units, or OTUs). NINJA takes advantage of the Burrows-Wheeler (BW) alignment using an artificial reference chromosome composed of concatenated reference sequences, the “concatesome,” as the BW input. Other features include automatic support for paired-end reads with arbitrary insert sizes. NINJA is also free and open source and implements several pre-filtering methods that elicit substantial speedup when coupled with existing tools. We applied NINJA to several published microbiome studies, obtaining accuracy similar to or better than previous reference-based OTU-picking methods while achieving an order of magnitude or more speedup and using a fraction of the memory footprint. NINJA is a complete pipeline that takes a FASTA-formatted input file and outputs a QIIME-formatted taxonomy-annotated BIOM file for an entire MiSeq run of human gut microbiome 16S genes in under 10 minutes on a dual-core laptop.     

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.     

SAP-A Sequence Mapping and Analyzing Program for Long Sequence Reads Alignment and Accurate Variants Discovery

The third-generation of sequencing technologies produces sequence reads of 1000 bp or more that may contain high polymorphism information. However, most currently available sequence analysis tools are developed specifically for analyzing short sequence reads. While the traditional Smith-Waterman (SW) algorithm can be used to map long sequence reads, its naive implementation is computationally infeasible. We have developed a new Sequence mapping and Analyzing Program (SAP) that implements a modified version of SW to speed up the alignment process. In benchmarks with simulated and real exon sequencing data and a real E. coli genome sequence data generated by the third-generation sequencing technologies, SAP outperforms currently available tools for mapping short and long sequence reads in both speed and proportion of captured reads. In addition, it achieves high accuracy in detecting SNPs and InDels in the simulated data. SAP is available at https://github.com/davidsun/SAP.     

Gene identification in novel eukaryotic genomes by self-training algorithm

Finding new protein-coding genes is one of the most important goals of eukaryotic genome sequencing projects. However, genomic organization of novel eukaryotic genomes is diverse and ab initio gene finding tools tuned up for previously studied species are rarely suitable for efficacious gene hunting in DNA sequences of a new genome. Gene identification methods based on cDNA and expressed sequence tag (EST) mapping to genomic DNA or those using alignments to closely related genomes rely either on existence of abundant cDNA and EST data and/or availability on reference genomes. Conventional statistical ab initio methods require large training sets of validated genes for estimating gene model parameters. In practice, neither one of these types of data may be available in sufficient amount until rather late stages of the novel genome sequencing. Nevertheless, we have shown that gene finding in eukaryotic genomes could be carried out in parallel with statistical models estimation directly from yet anonymous genomic DNA. The suggested method of parallelization of gene prediction with the model parameters estimation follows the path of the iterative Viterbi training. Rounds of genomic sequence labeling into coding and non-coding regions are followed by the rounds of model parameters estimation. Several dynamically changing restrictions on the possible range of model parameters are added to filter out fluctuations in the initial steps of the algorithm that could redirect the iteration process away from the biologically relevant point in parameter space. Tests on well-studied eukaryotic genomes have shown that the new method performs comparably or better than conventional methods where the supervised model training precedes the gene prediction step. Several novel genomes have been analyzed and biologically interesting findings are discussed. Thus, a self-training algorithm that had been assumed feasible only for prokaryotic genomes has now been developed for ab initio eukaryotic gene identification.     

PSAR: measuring multiple sequence alignment reliability by probabilistic sampling

Multiple sequence alignment, which is of fundamental importance for comparative genomics, is a difficult problem and error-prone. Therefore, it is essential to measure the reliability of the alignments and incorporate it into downstream analyses. We propose a new probabilistic sampling-based alignment reliability (PSAR) score. Instead of relying on heuristic assumptions, such as the correlation between alignment quality and guide tree uncertainty in progressive alignment methods, we directly generate suboptimal alignments from an input multiple sequence alignment by a probabilistic sampling method, and compute the agreement of the input alignment with the suboptimal alignments as the alignment reliability score. We construct the suboptimal alignments by an approximate method that is based on pairwise comparisons between each single sequence and the sub-alignment of the input alignment where the chosen sequence is left out. By using simulation-based benchmarks, we find that our approach is superior to existing ones, supporting that the suboptimal alignments are highly informative source for assessing alignment reliability. We apply the PSAR method to the alignments in the UCSC Genome Browser to measure the reliability of alignments in different types of regions, such as coding exons and conserved non-coding regions, and use it to guide cross-species conservation study.     

A word-oriented approach to alignment validation

MOTIVATION: Multiple sequence alignment at the level of whole proteomes requires a high degree of automation, precluding the use of traditional validation methods such as manual curation. Since evolutionary models are too general to describe the history of each residue in a protein family, there is no single algorithm/model combination that can yield a biologically or evolutionarily optimal alignment. We propose a 'shotgun' strategy where many different algorithms are used to align the same family, and the best of these alignments is then chosen with a reliable objective function. We present WOOF, a novel 'word-oriented' objective function that relies on the identification and scoring of conserved amino acid patterns (words) between pairs of sequences. RESULTS: Tests on a subset of reference protein alignments from BAliBASE showed that WOOF tended to rank the (manually curated) reference alignment highest among 1060 alternative (automatically generated) alignments for a majority of protein families. Among the automated alignments, there was a strong positive relationship between the WOOF score and similarity to the reference alignment. The speed of WOOF and its independence from explicit considerations of three-dimensional structure make it an excellent tool for analyzing large numbers of protein families. AVAILABILITY: On request from the authors.     

Long-read sequence assembly: a technical evaluation in barley

Sequence assembly of large and repeat-rich plant genomes has been challenging, requiring substantial computational resources and often several complementary sequence assembly and genome mapping approaches. The recent development of fast and accurate long-read sequencing by circular consensus sequencing (CCS) on the PacBio platform may greatly increase the scope of plant pan-genome projects. Here, we compare current long-read sequencing platforms regarding their ability to rapidly generate contiguous sequence assemblies in pan-genome studies of barley (Hordeum vulgare). Most long-read assemblies are clearly superior to the current barley reference sequence based on short-reads. Assemblies derived from accurate long reads excel in most metrics, but the CCS approach was the most cost-effective strategy for assembling tens of barley genomes. A downsampling analysis indicated that 20-fold CCS coverage can yield very good sequence assemblies, while even five-fold CCS data may capture the complete sequence of most genes. We present an updated reference genome assembly for barley with near-complete representation of the repeat-rich intergenic space. Long-read assembly can underpin the construction of accurate and complete sequences of multiple genomes of a species to build pan-genome infrastructures in Triticeae crops and their wild relatives.

A greatly improved reference genome sequence of barley was assembled from accurate long reads.     

Perm-seq: Mapping Protein-DNA Interactions in Segmental Duplication and Highly Repetitive Regions of Genomes with Prior-Enhanced Read Mapping

Segmental duplications and other highly repetitive regions of genomes contribute significantly to cells’ regulatory programs. Advancements in next generation sequencing enabled genome-wide profiling of protein-DNA interactions by chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq). However, interactions in highly repetitive regions of genomes have proven difficult to map since short reads of 50–100 base pairs (bps) from these regions map to multiple locations in reference genomes. Standard analytical methods discard such multi-mapping reads and the few that can accommodate them are prone to large false positive and negative rates. We developed Perm-seq, a prior-enhanced read allocation method for ChIP-seq experiments, that can allocate multi-mapping reads in highly repetitive regions of the genomes with high accuracy. We comprehensively evaluated Perm-seq, and found that our prior-enhanced approach significantly improves multi-read allocation accuracy over approaches that do not utilize additional data types. The statistical formalism underlying our approach facilitates supervising of multi-read allocation with a variety of data sources including histone ChIP-seq. We applied Perm-seq to 64 ENCODE ChIP-seq datasets from GM12878 and K562 cells and identified many novel protein-DNA interactions in segmental duplication regions. Our analysis reveals that although the protein-DNA interactions sites are evolutionarily less conserved in repetitive regions, they share the overall sequence characteristics of the protein-DNA interactions in non-repetitive regions.     

Anchored pseudo-de novo assembly of human genomes identifies extensive sequence variation from unmapped sequence reads

The human genome reference (HGR) completion marked the genomics era beginning, yet despite its utility universal application is limited by the small number of individuals used in its development. This is highlighted by the presence of high-quality sequence reads failing to map within the HGR. Sequences failing to map generally represent 2–5 % of total reads, which may harbor regions that would enhance our understanding of population variation, evolution, and disease. Alternatively, complete de novo assemblies can be created, but these effectively ignore the groundwork of the HGR. In an effort to find a middle ground, we developed a bioinformatic pipeline that maps paired-end reads to the HGR as separate single reads, exports unmappable reads, de novo assembles these reads per individual and then combines assemblies into a secondary reference assembly used for comparative analysis. Using 45 diverse 1000 Genomes Project individuals, we identified 351,361 contigs covering 195.5 Mb of sequence unincorporated in GRCh38. 30,879 contigs are represented in multiple individuals with ~40 % showing high sequence complexity. Genomic coordinates were generated for 99.9 %, with 52.5 % exhibiting high-quality mapping scores. Comparative genomic analyses with archaic humans and primates revealed significant sequence alignments and comparisons with model organism RefSeq gene datasets identified novel human genes. If incorporated, these sequences will expand the HGR, but more importantly our data highlight that with this method low coverage (~10–20×) next-generation sequencing can still be used to identify novel unmapped sequences to explore biological functions contributing to human phenotypic variation, disease and functionality for personal genomic medicine.     

Predicting the accuracy of multiple sequence alignment algorithms by using computational intelligent techniques

Multiple sequence alignments (MSAs) have become one of the most studied approaches in bioinformatics to perform other outstanding tasks such as structure prediction, biological function analysis or next-generation sequencing. However, current MSA algorithms do not always provide consistent solutions, since alignments become increasingly difficult when dealing with low similarity sequences. As widely known, these algorithms directly depend on specific features of the sequences, causing relevant influence on the alignment accuracy. Many MSA tools have been recently designed but it is not possible to know in advance which one is the most suitable for a particular set of sequences. In this work, we analyze some of the most used algorithms presented in the bibliography and their dependences on several features. A novel intelligent algorithm based on least square support vector machine is then developed to predict how accurate each alignment could be, depending on its analyzed features. This algorithm is performed with a dataset of 2180 MSAs. The proposed system first estimates the accuracy of possible alignments. The most promising methodologies are then selected in order to align each set of sequences. Since only one selected algorithm is run, the computational time is not excessively increased.