Short Read Alignment Research Articles

Fast and accurate short-read alignment with hybrid hash-tree data structure

Rapidly increasing the amount of short-read data generated by NGSs (new-generation sequencers) calls for the development of fast and accurate read alignment programs. The programs based on the hash table (BLAST) and Burrows-Wheeler transform (bwa-mem) are used, and the latter is known to give superior performance. We here present a new algorithm, a hybrid of hash table and suffix tree, which we designed to speed up the alignment of short reads against large reference sequences such as the human genome. The total turnaround time for processing one human genome sample (read depth of 30) is just 31 min with our system while that was more than 25 h with bwa-mem/gatk. The time for the aligner alone is 28 min for our system but around 2 h for bwa-mem. Our new algorithm is 4.4 times faster than bwa-mem while achieving similar accuracy. Variant calling and other downstream analyses after the alignment can be done with open-source tools such as SAMtools and Genome Analysis Toolkit (gatk) packages, as well as our own fast variant caller, which is well parallelized and much faster than gatk.

The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations

Marine microbial eukaryotes (protists) perform essential metabolic functions in oceanic ecosystems. The diversity of protist functions remains poorly understood as few species have been isolated in laboratory settings. Metatranscriptomes provide an invaluable tool for exploring protist diversity and genetic capacities within their natural habitats. Here, we introduce the North Pacific Eukaryotic Gene Catalog, a compilation of metatranscriptome data derived from a total of 261 metatranscriptomes: 169 metatranscriptomes were derived from samples collected on three meridional surface transects along 158°W, each spanning ~20 degrees of latitude from the North Pacific Subtropical Gyre (NPSG) to the North Pacific Transition Zone (NPTZ); 92 metatranscriptomes were derived from two diel-resolved field studies, one in the NPSG at 157°W, 23°N, one in the NPTZ at 158°W, 41°N. The metatranscriptome sequences were de novo assembled into 175 assemblies and pooled into five datasets each containing between 22 M and 49 M contigs clustered at 99% protein identity. Assemblies were annotated by taxonomy and function, and enumerated by short read alignment. All data are available in the Zenodo repository, with underlying code available on github.

Personalized pangenome references.

Pangenomes reduce reference bias by representing genetic diversity better than a single reference sequence. Yet when comparing a sample to a pangenome, variants in the pangenome that are not part of the sample can be misleading, for example, causing false read mappings. These irrelevant variants are generally rarer in terms of allele frequency, and have previously been dealt with by filtering rare variants. However, this blunt heuristic both fails to remove some irrelevant variants and removes many relevant variants. We propose a new approach that imputes a personalized pangenome subgraph by sampling local haplotypes according to k-mer counts in the reads. We implement the approach in the vg toolkit ( https://github.com/vgteam/vg ) for the Giraffe short-read aligner and compare its accuracy to state-of-the-art methods using human pangenome graphs from the Human Pangenome Reference Consortium. This reduces small variant genotyping errors by four times relative to the Genome Analysis Toolkit and makes short-read structural variant genotyping of known variants competitive with long-read variant discovery methods.

Extracellular vesicles carry transcriptional 'dark matter' revealing tissue-specific information.

From eukaryotes to prokaryotes, all cells secrete extracellular vesicles (EVs) as part of their regular homeostasis, intercellular communication, and cargo disposal. Accumulating evidence suggests that small EVs carry functional small RNAs, potentially serving as extracellular messengers and liquid-biopsy markers. Yet, the complete transcriptomic landscape of EV-associated small RNAs during disease progression is poorly delineated due to critical limitations including the protocols used for sequencing, suboptimal alignment of short reads (20-50nt), and uncharacterized genome annotations-often denoted as the 'dark matter' of the genome. In this study, we investigate the EV-associated small unannotated RNAs that arise from endogenous genes and are part of the genomic 'dark matter', which may play a key emerging role in regulating gene expression and translational mechanisms. To address this, we created a distinct small RNAseq dataset from human prostate cancer & benign tissues, and EVs derived from blood (pre- & post-prostatectomy), urine, and human prostate carcinoma epithelial cell line. We then developed an unsupervised data-based bioinformatic pipeline that recognizes biologically relevant transcriptional signals irrespective of their genomic annotation. Using this approach, we discovered distinct EV-RNA expression patterns emerging from the un-annotated genomic regions (UGRs) of the transcriptomes associated with tissue-specific phenotypes. We have named these novel EV-associated small RNAs as 'EV-UGRs' or "EV-dark matter". Here, we demonstrate that EV-UGR gene expressions are downregulated by ∼100 fold (FDR<0.05) in the circulating serum EVs from aggressive prostate cancer subjects. Remarkably, these EV-UGRs expression signatures were regained (upregulated) after radical prostatectomy in the same follow-up patients. Finally, we developed a stem-loop RT-qPCR assay that validated prostate cancer-specific EV-UGRs for selective fluid-based diagnostics. Overall, using an unsupervised data driven approach, we investigate the 'dark matter' of EV-transcriptome and demonstrate that EV-UGRs carry tissue-specific Information that significantly alters pre- and post-prostatectomy in the prostate cancer patients. Although further validation in randomized clinical trials is required, this new class of EV-RNAs hold promise in liquid-biopsy by avoiding highly invasive biopsy procedures in prostate cancer.

The Structure of Simple Satellite Variation in the Human Genome and Its Correlation With Centromere Ancestry.

Although repetitive DNA forms much of the human genome, its study is challenging due to limitations in assembly and alignment of repetitive short-reads. We have deployed k-Seek, software that detects tandem repeats embedded in single reads, on 2,504 human genomes from the 1,000 Genomes Project to quantify the variation and abundance of simple satellites (repeat units <20 bp). We find that the ancestral monomer of Human Satellite 3 makes up the largest portion of simple satellite content in humans (mean of ∼8 Mb). We discovered ∼50,000 rare tandem repeats that are not detected in the T2T-CHM13v2.0 assembly, including undescribed variants of telomericand pericentromeric repeats. We find broad homogeneity of the most abundant repeats across populations, except for AG-rich repeats which are more abundant in African individuals. We also find cliques of highly similar AG- and AT-rich satellites that are interspersed and form higher-order structures that covary in copy number across individuals, likely through concerted amplification via unequal exchange. Finally, we use pericentromeric polymorphisms to estimate centromeric genetic relatedness between individuals and find a strong predictive relationship between centromeric lineages and pericentromeric simple satellite abundances. In particular, ancestral monomers of Human Satellite 2 and Human Satellite 3 abundances correlate with clusters of centromeric ancestry on chromosome 16 and chromosome 9, with some clusters structured by population. These results provide new descriptions of the population dynamics that underlie the evolution of simple satellites in humans.

A new Finnish flavor of feline coat coloration, "salmiak," is associated with a 95-kb deletion downstream of the KIT gene.

Cats with a distinctive white hair pattern of unknown molecular cause have been discovered in the Finnish domestic cat population. Based on the unique appearance of these cats, we have named this phenotype salmiak ("salty licorice"). The use of a commercially available panel test to genotype four salmiak-colored cats revealed the absence of all known variants associated with white-haired phenotypic loci: full White (W), Spotting (Ws) and the Birman white Gloves associated (wg) allele of the KIT proto-oncogene (KIT) gene. Whole-genome sequencing on two salmiak-colored cats was conducted to search for candidate causal variants in the KIT gene. Despite a lack of coding variants, visual inspection of the short read alignments revealed a large ~95 kb deletion located ~65 kb downstream of the KIT gene in the salmiak cats. Additional PCR genotyping of 180 domestic cats and three salmiak-colored cats confirmed the homozygous derived variant genotype fully concordant with the salmiak phenotype. We suggest the newly identified variant be designated as wsal for "w salmiak".

The impact of FASTQ and alignment read order on structural variant calling from long-read sequencing data.

Structural variant (SV) calling from DNA sequencing data has been challenging due to several factors, including the ambiguity of short-read alignments, multiple complex SVs in the same genomic region, and the lack of "truth" datasets for benchmarking. Additionally, caller choice, parameter settings, and alignment method are known to affect SV calling. However, the impact of FASTQ read order on SV calling has not been explored for long-read data. Here, we used PacBio DNA sequencing data from 15 Caenorhabditis elegans strains and four Arabidopsis thaliana ecotypes to evaluate the sensitivity of different SV callers on FASTQ read order. Comparisons of variant call format files generated from the original and permutated FASTQ files demonstrated that the order of input data affected the SVs predicted by each caller. In particular, pbsv was highly sensitive to the order of the input data, especially at the highest depths where over 70% of the SV calls generated from pairs of differently ordered FASTQ files were in disagreement. These demonstrate that read order sensitivity is a complex, multifactorial process, as the differences observed both within and between species varied considerably according to the specific combination of aligner, SV caller, and sequencing depth. In addition to the SV callers being sensitive to the input data order, the SAMtools alignment sorting algorithm was identified as a source of variability following read order randomization. The results of this study highlight the sensitivity of SV calling on the order of reads encoded in FASTQ files, which has not been recognized in long-read approaches. These findings have implications for the replication of SV studies and the development of consistent SV calling protocols. Our study suggests that researchers should pay attention to the input order sensitivity of read alignment sorting methods when analyzing long-read sequencing data for SV calling, as mitigating a source of variability could facilitate future replication work. These results also raise important questions surrounding the relationship between SV caller read order sensitivity and tool performance. Therefore, tool developers should also consider input order sensitivity as a potential source of variability during the development and benchmarking of new and improved methods for SV calling.

Pangenome-genotyped structural variation improves molecular phenotype mapping in cattle.

Expression and splicing quantitative trait loci (e/sQTL) are large contributors to phenotypic variability. Achieving sufficient statistical power for e/sQTL mapping requires large cohorts with both genotypes and molecular phenotypes, and so, the genomic variation is often called from short-read alignments, which are unable to comprehensively resolve structural variation. Here we build a pangenome from 16 HiFi haplotype-resolved cattle assemblies to identify small and structural variation and genotype them with PanGenie in 307 short-read samples. We find high (>90%) concordance of PanGenie-genotyped and DeepVariant-called small variation and confidently genotype close to 21 million small and 43,000 structural variants in the larger population. We validate 85% of these structural variants (with MAF > 0.1) directly with a subset of 25 short-read samples that also have medium coverage HiFi reads. We then conduct e/sQTL mapping with this comprehensive variant set in a subset of 117 cattle that have testis transcriptome data, and find 92 structural variants as causal candidates for eQTL and 73 for sQTL. We find that roughly half of the top associated structural variants affecting expression or splicing are transposable elements, such as SV-eQTL for STN1 and MYH7 and SV-sQTL for CEP89 and ASAH2 Extensive linkage disequilibrium between small and structural variation results in only 28 additional eQTL and 17 sQTL discovered when including SVs, although many top associated SVs are compelling candidates.

An imputed ancestral reference genome for the Mycobacterium tuberculosis complex better captures structural genomic diversity for reference-based alignment workflows.

Reference-based alignment of short-reads is a widely used technique in genomic analysis of the Mycobacterium tuberculosis complex (MTBC) and the choice of reference sequence impacts the interpretation of analyses. The most widely used reference genomes include the ATCC type strain (H37Rv) and the putative MTBC ancestral sequence of Comas et al. both of which are based on a lineage 4 sequence. As such, these reference sequences do not capture all of the structural variation known to be present in the ancestor of the MTBC. To better represent the base of the MTBC, we generated an imputed ancestral genomic sequence, termed MTBC0 from reference-free alignments of closed MTBC genomes. When used as a reference sequence in alignment workflows, MTBC0 mapped more short sequencing reads and called more pairwise SNPs relative to the Comas et al. sequence while exhibiting minimal impact on the overall phylogeny of MTBC. The results also show that MTBC0 provides greater fidelity in capturing genomic variation and allows for the inclusion of regions absent from H37Rv in standard MTBC workflows without additional steps. The use of MTBC0 as an ancestral reference sequence in standard workflows modestly improved read mapping, SNP calling and intuitively facilitates the study of structural variation and evolution in MTBC.

A pangenome graph reference of 30 chicken genomes allows genotyping of large and complex structural variants

BackgroundThe red junglefowl, the wild outgroup of domestic chickens, has historically served as a reference for genomic studies of domestic chickens. These studies have provided insight into the etiology of traits of commercial importance. However, the use of a single reference genome does not capture diversity present among modern breeds, many of which have accumulated molecular changes due to drift and selection. While reference-based resequencing is well-suited to cataloging simple variants such as single-nucleotide changes and short insertions and deletions, it is mostly inadequate to discover more complex structural variation in the genome.MethodsWe present a pangenome for the domestic chicken consisting of thirty assemblies of chickens from different breeds and research lines.ResultsWe demonstrate how this pangenome can be used to catalog structural variants present in modern breeds and untangle complex nested variation. We show that alignment of short reads from 100 diverse wild and domestic chickens to this pangenome reduces reference bias by 38%, which affects downstream genotyping results. This approach also allows for the accurate genotyping of a large and complex pair of structural variants at the K feathering locus using short reads, which would not be possible using a linear reference.ConclusionsWe expect that this new paradigm of genomic reference will allow better pinpointing of exact mutations responsible for specific phenotypes, which will in turn be necessary for breeding chickens that meet new sustainability criteria and are resilient to quickly evolving pathogen threats.

Data mining reveals tissue-specific expression and host lineage-associated forms of Apis mellifera filamentous virus.

Apis mellifera filamentous virus (AmFV) is a large double-stranded DNA virus of uncertain phylogenetic position that infects honey bees (Apis mellifera). Little is known about AmFV evolution or molecular aspects of infection. Accurate annotation of open-reading frames (ORFs) is challenged by weak homology to other known viruses. This study was undertaken to evaluate ORFs (including coding-frame conservation, codon bias, and purifying selection), quantify genetic variation within AmFV, identify host characteristics that covary with infection rate, and examine viral expression patterns in different tissues. Short-read data were accessed from the Sequence Read Archive (SRA) of the National Center for Biotechnology Information (NCBI). Sequence reads were downloaded from accessions meeting search criteria and scanned for kmers representative of AmFV genomic sequence. Samples with kmer counts above specified thresholds were downloaded in full for mapping to reference sequences and de novo assembly. At least three distinct evolutionary lineages of AmFV exist. Clade 1 predominates in Europe but in the Americas and Africa it is replaced by the other clades as infection level increases in hosts. Only clade 3 was found at high relative abundance in hosts with African ancestry, whereas all clades achieved high relative abundance in bees of non-African ancestry. In Europe and Africa, clade 2 was generally detected only in low-level infections but was locally dominant in some North American samples. The geographic distribution of clade 3 was consistent with an introduction to the Americas with 'Africanized' honey bees in the 1950s. Localized genomic regions of very high nucleotide divergence in individual isolates suggest recombination with additional, as-yet unidentified AmFV lineages. A set of 155 high-confidence ORFs was annotated based on evolutionary conservation in six AmFV genome sequences representative of the three clades. Pairwise protein-level identity averaged 94.6% across ORFs (range 77.1-100%), which generally exhibited low evolutionary rates and moderate to strong codon bias. However, no robust example of positive diversifying selection on coding sequence was found in these alignments. Most of the genome was detected in RNA short-read alignments. Transcriptome assembly often yielded contigs in excess of 50 kb and containing ORFs in both orientations, and the termini of long transcripts were associated with tandem repeats. Lower levels of AmFV RNA were detected in brain tissue compared to abdominal tissue, and a distinct set of ORFs had minimal to no detectable expression in brain tissue. A scan of DNA accessions from the parasitic mite Varroa destructor was inconclusive with respect to replication in that species. Collectively, these results expand our understanding of this enigmatic virus, revealing transcriptional complexity and co-evolutionary associations with host lineage.

Reference Genome Choice and Filtering Thresholds Jointly Influence Phylogenomic Analyses.

Molecular phylogenies are a cornerstone of modern comparative biology and are commonly employed to investigate a range of biological phenomena, such as diversification rates, patterns in trait evolution, biogeography, and community assembly. Recent work has demonstrated that significant biases may be introduced into downstream phylogenetic analyses from processing genomic data; however, it remains unclear whether there are interactions among bioinformatic parameters or biases introduced through the choice of reference genome for sequence alignment and variant calling. We address these knowledge gaps by employing a combination of simulated and empirical data sets to investigate the extent to which the choice of reference genome in upstream bioinformatic processing of genomic data influences phylogenetic inference, as well as the way that reference genome choice interacts with bioinformatic filtering choices and phylogenetic inference method. We demonstrate that more stringent minor allele filters bias inferred trees away from the true species tree topology, and that these biased trees tend to be more imbalanced and have a higher center of gravity than the true trees. We find the greatest topological accuracy when filtering sites for minor allele count (MAC) >3-4 in our 51-taxa data sets, while tree center of gravity was closest to the true value when filtering for sites with MAC >1-2. In contrast, filtering for missing data increased accuracy in the inferred topologies; however, this effect was small in comparison to the effect of minor allele filters and may be undesirable due to a subsequent mutation spectrum distortion. The bias introduced by these filters differs based on the reference genome used in short read alignment, providing further support that choosing a reference genome for alignment is an important bioinformatic decision with implications for downstream analyses. These results demonstrate that attributes of the study system and dataset (and their interaction) add important nuance for how best to assemble and filter short-read genomic data for phylogenetic inference.

RAPIDx: High-Performance ReRAM Processing In-Memory Accelerator for Sequence Alignment

Genome sequence alignment is the core of many biological applications. The advancement of sequencing technologies produces a tremendous amount of data, making sequence alignment a critical bottleneck in bioinformatics analysis. The existing hardware accelerators for alignment suffer from limited on-chip memory, costly data movement, and poorly optimized alignment algorithms. They cannot afford to concurrently process the massive amount of data generated by sequencing machines. In this paper, we propose a ReRAM-based accelerator, RAPIDx, using processing in-memory (PIM) for sequence alignment. RAPIDx achieves superior efficiency and performance via software-hardware co-design. First, we propose an adaptive banded parallelism alignment algorithm suitable for PIM architecture. Compared to the original dynamic programming-based alignment, the proposed algorithm significantly reduces the required complexity, data bit width, and memory footprint at the cost of negligible accuracy degradation. Then we propose the efficient PIM architecture that implements the proposed algorithm. The data flow in RAPIDx achieves four-level parallelism and we design an in-situ alignment computation flow in ReRAM, delivering $5.5$-$9.7\times$ efficiency and throughput improvements compared to our previous PIM design, RAPID. The proposed RAPIDx is reconfigurable to serve as a co-processor integrated into existing genome analysis pipeline to boost sequence alignment or edit distance calculation. On short-read alignment, RAPIDx delivers $131.1\times$ and $46.8\times$ throughput improvements over state-of-the-art CPU and GPU libraries, respectively. As compared to ASIC accelerators for long-read alignment, the performance of RAPIDx is $1.8$-$2.9\times$ higher.

Short Sequence Aligner Benchmarking for Chromatin Research.

Much of today's molecular science revolves around next-generation sequencing. Frequently, the first step in analyzing such data is aligning sequencing reads to a reference genome. This step is often taken for granted, but any analysis downstream of the alignment will be affected by the aligner's ability to correctly map sequences. In most cases, for research into chromatin structure and nucleosome positioning, ATAC-seq, ChIP-seq, and MNase-seq experiments use short read lengths. How well aligners manage these reads is critical. Most aligner programs will output mapped reads and unmapped reads. However, from a biological point of view, reads will fall into one of three categories: correctly mapped, incorrectly mapped, and unmapped. While increased sequencing depth can often compensate for unmapped reads, incorrectly and correctly mapped reads appear algorithmically identical but can produce biologically significant alterations in the results. For this reason, we are benchmarking various alignment programs to determine their propensity to incorrectly map short reads. As short-read alignment is an important step in ATAC-seq, ChIP-seq, and MNase-seq experiments, caution should be taken in mapping reads to ensure that the most accurate conclusions can be made from the data generated. Our analysis is intended to help investigators new to the field pick the alignment program best suited for their experimental conditions. In general, the aligners we tested performed well. BWA, Bowtie2, and Chromap were all exceptionally accurate, and we recommend using them. Furthermore, we show that longer read lengths do in fact lead to more accurate mappings.

BiocMAP: a Bioconductor-friendly, GPU-accelerated pipeline for bisulfite-sequencing data

BackgroundBisulfite sequencing is a powerful tool for profiling genomic methylation, an epigenetic modification critical in the understanding of cancer, psychiatric disorders, and many other conditions. Raw data generated by whole genome bisulfite sequencing (WGBS) requires several computational steps before it is ready for statistical analysis, and particular care is required to process data in a timely and memory-efficient manner. Alignment to a reference genome is one of the most computationally demanding steps in a WGBS workflow, taking several hours or even days with commonly used WGBS-specific alignment software. This naturally motivates the creation of computational workflows that can utilize GPU-based alignment software to greatly speed up the bottleneck step. In addition, WGBS produces raw data that is large and often unwieldy; a lack of memory-efficient representation of data by existing pipelines renders WGBS impractical or impossible to many researchers.ResultsWe present BiocMAP, a Bioconductor-friendly methylation analysis pipeline consisting of two modules, to address the above concerns. The first module performs computationally-intensive read alignment using Arioc, a GPU-accelerated short-read aligner. Since GPUs are not always available on the same computing environments where traditional CPU-based analyses are convenient, the second module may be run in a GPU-free environment. This module extracts and merges DNA methylation proportions—the fractions of methylated cytosines across all cells in a sample at a given genomic site. Bioconductor-based output objects in R utilize an on-disk data representation to drastically reduce required main memory and make WGBS projects computationally feasible to more researchers.ConclusionsBiocMAP is implemented using Nextflow and available at http://research.libd.org/BiocMAP/. To enable reproducible analysis across a variety of typical computing environments, BiocMAP can be containerized with Docker or Singularity, and executed locally or with the SLURM or SGE scheduling engines. By providing Bioconductor objects, BiocMAP’s output can be integrated with powerful analytical open source software for analyzing methylation data.

Short-read aligner performance in germline variant identification.

Read alignment is an essential first step in the characterization of DNA sequence variation. The accuracy of variant calling results depends not only on the quality of read alignment and variant calling software but also on the interaction between these complex software tools. In this review, we evaluate short-read aligner performance with the goal of optimizing germline variant calling accuracy. We examine the performance of three general-purpose short-read aligners - BWA-MEM, Bowtie 2, and Arioc - in conjunction with three germline variant callers: DeepVariant, FreeBayes, and GATK HaplotypeCaller. We discuss the behavior of the read aligners with regard to the data elements on which the variant callers rely, and illustrate how the runtime configurations of these software tools combine to affect variant calling performance. The quick brown fox jumps over the lazy dog. Supplementary information is available at Bioinformatics online.

SLHSD: hybrid scaffolding method based on short and long reads.

In genome assembly, scaffolding can obtain more complete and continuous scaffolds. Current scaffolding methods usually adopt one type of read to construct a scaffold graph and then orient and order contigs. However, scaffolding with the strengths of two or more types of reads seems to be a better solution to some tricky problems. Combining the advantages of different types of data is significant for scaffolding. Here, a hybrid scaffolding method (SLHSD) is present that simultaneously leverages the precision of short reads and the length advantage of long reads. Building an optimal scaffold graph is an important foundation for getting scaffolds. SLHSD uses a new algorithm that combines long and short read alignment information to determine whether to add an edge and how to calculate the edge weight in a scaffold graph. In addition, SLHSD develops a strategy to ensure that edges with high confidence can be added to the graph with priority. Then, a linear programming model is used to detect and remove remaining false edges in the graph. We compared SLHSD with other scaffolding methods on five datasets. Experimental results show that SLHSD outperforms other methods. The open-source code of SLHSD is available at https://github.com/luojunwei/SLHSD.

Diversity of fatty acid biosynthesis genes across the soybean pangenome.

Soybean (Glycine max) is a major crop that contributes more than half of global oilseed production. Much research has been directed towards improvement of the fatty acid profile of soybean seeds through marker assisted breeding. Recently published soybean pangenomes, based on thousands of soybean lines, provide an opportunity to identify new alleles that may be involved in fatty acid biosynthesis. In this study, we identify fatty acid biosynthesis genes in soybean pangenomes based on sequence identity with known genes and examine their sequence diversity across diverse soybean collections. We find three possible instances of a gene missing in wild soybean, including FAD8 and FAD2-2D, which may be involved in oleic and linoleic acid desaturation, respectively, although we recommend follow-up research to verify the absence of these genes. More than half of the 53 fatty acid biosynthesis genes identified contained missense variants, including one linked with a previously identified QTL for oil quality. These variants were present in multiple studies based on either short read mappings or alignment of reference grade genomes. Missense variants were found in previously characterized genes including FAD2-1A and FAD2-1B, both of which are involved in desaturation of oleic acid, as well as uncharacterized candidate fatty acid biosynthesis genes. We find that the frequency of missense alleles in fatty acid biosynthesis genes has been reduced significantly more than the global average frequency of missense mutations during domestication, and missense variation in some genes is near absent in modern cultivars. This could be due to the selection for fatty acid profiles in seed, though future work should be conducted towards understanding the phenotypic impacts of these variants.

ViReMa: a virus recombination mapper of next-generation sequencing data characterizes diverse recombinant viral nucleic acids.

Genetic recombination is a tremendous source of intrahost diversity in viruses and is critical for their ability to rapidly adapt to new environments or fitness challenges. While viruses are routinely characterized using high-throughput sequencing techniques, characterizing the genetic products of recombination in next-generation sequencing data remains a challenge. Viral recombination events can be highly diverse and variable in nature, including simple duplications and deletions, or more complex events such as copy/snap-back recombination, intervirus or intersegment recombination, and insertions of host nucleic acids. Due to the variable mechanisms driving virus recombination and the different selection pressures acting on the progeny, recombination junctions rarely adhere to simple canonical sites or sequences. Furthermore, numerous different events may be present simultaneously in a viral population, yielding a complex mutational landscape. We have previously developed an algorithm called ViReMa (Virus Recombination Mapper) that bootstraps the bowtie short-read aligner to capture and annotate a wide range of recombinant species found within virus populations. Here, we have updated ViReMa to provide an "error density" function designed to accurately detect recombination events in the longer reads now routinely generated by the Illumina platforms and provide output reports for multiple types of recombinant species using standardized formats. We demonstrate the utility and flexibility of ViReMa in different settings to report deletion events in simulated data from Flock House virus, copy-back RNA species in Sendai viruses, short duplication events in HIV, and virus-to-host recombination in an archaeal DNA virus.

Population differentiated copy number variation between Eurasian wild boar and domesticated pig populations

Sus scrofa is a globally distributed livestock species that still maintains two different ways of life: wild and domesticated. Herein, we detected copy number variation (CNV) of 328 animals using short read alignment on Sscrofa11.1. We compared CNV among five groups of porcine populations: Asian domesticated (AD), European domesticated (ED), Asian wild (AW), European wild (EW), and Near Eastern wild (NEW). In total, 21,673 genes were identified on 154,872 copy number variation region (CNVR). Differences in gene copy numbers between populations were measured by considering the variance-based value {V}_{ST} and the one-way ANOVA test followed by Scheffe test. As a result, 111 genes were suggested as copy number variable genes. Abnormally gained copy number on EEA1 in all populations was suggested the presence of minor CNV in the reference genome assembly, Sscrofa11.1. Copy number variable genes were related to meat quality, immune response, and reproduction traits. Hierarchical clustering of all individuals and mean pairwise {V}_{ST} in breed level were visualized genetic relationship of 328 individuals and 56 populations separately. Our findings have shown how the complex history of pig evolution appears in genome-wide CNV of various populations with different regions and lifestyles.