Whole-genome re-sequencing of non-model organisms: lessons from unmapped reads

Unmapped reads are often discarded from the analysis of whole-genome re-sequencing, but new biological information and insights can be uncovered through their analysis. In this paper, we investigate unmapped reads from the re-sequencing data of 33 pea aphid genomes from individuals specialized on different host plants. The unmapped reads for each individual were retrieved following mapping to the Acyrthosiphon pisum reference genome and its mitochondrial and symbiont genomes. These sets of unmapped reads were then cross-compared, revealing that a significant number of these unmapped sequences were conserved across individuals. Interestingly, sequences were most commonly shared between individuals adapted to the same host plant, suggesting that these sequences may contribute to the divergence between host plant specialized biotypes. Analysis of the contigs obtained from assembling the unmapped reads pooled by biotype allowed us to recover some divergent genomic regions previously excluded from analysis and to discover putative novel sequences of A. pisum and its symbionts. In conclusion, this study emphasizes the interest of the unmapped component of re-sequencing data sets and the potential loss of important information. We here propose strategies to aid the capture and interpretation of this information.     

Genome‐wide characterization and expression analysis of genetic variants in sweet orange

Heterozyosity is an important feature of many plant genomes, and is related to heterosis. Sweet orange, a highly heterozygous species, is thought to have originated from an inter‐species hybrid between pummelo and mandarin. To investigate the heterozygosity of the sweet orange genome and examine how this heterozygosity affects gene expression, we characterized the genome of Valencia orange for single nucleotide variations (SNVs), small insertions and deletions (InDels) and structural variations (SVs), and determined their functional effects on protein‐coding genes and non‐coding sequences. Almost half of the genes containing large‐effect SNVs and InDels were expressed in a tissue‐specific manner. We identified 3542 large SVs (>50 bp), including deletions, insertions and inversions. Most of the 296 genes located in large‐deletion regions showed low expression levels. RNA‐Seq reads and DNA sequencing reads revealed that the alleles of 1062 genes were differentially expressed. In addition, we detected approximately 42 Mb of contigs that were not found in the reference genome of a haploid sweet orange by de novo assembly of unmapped reads, and annotated 134 protein‐coding genes within these contigs. We discuss how this heterozygosity affects the quality of genome assembly. This study advances our understanding of the genome architecture of sweet orange, and provides a global view of gene expression at heterozygous loci.     

Identifying micro-inversions using high-throughput sequencing reads

Background

The identification of inversions of DNA segments shorter than read length (e.g., 100 bp), defined as micro-inversions (MIs), remains challenging for next-generation sequencing reads. It is acknowledged that MIs are important genomic variation and may play roles in causing genetic disease. However, current alignment methods are generally insensitive to detect MIs. Here we develop a novel tool, MID (Micro-Inversion Detector), to identify MIs in human genomes using next-generation sequencing reads.

Results

The algorithm of MID is designed based on a dynamic programming path-finding approach. What makes MID different from other variant detection tools is that MID can handle small MIs and multiple breakpoints within an unmapped read. Moreover, MID improves reliability in low coverage data by integrating multiple samples. Our evaluation demonstrated that MID outperforms Gustaf, which can currently detect inversions from 30 bp to 500 bp.

Conclusions

To our knowledge, MID is the first method that can efficiently and reliably identify MIs from unmapped short next-generation sequencing reads. MID is reliable on low coverage data, which is suitable for large-scale projects such as the 1000 Genomes Project (1KGP). MID identified previously unknown MIs from the 1KGP that overlap with genes and regulatory elements in the human genome. We also identified MIs in cancer cell lines from Cancer Cell Line Encyclopedia (CCLE). Therefore our tool is expected to be useful to improve the study of MIs as a type of genetic variant in the human genome. The source code can be downloaded from: http://cqb.pku.edu.cn/ZhuLab/MID.

     

Whole Genome Sequence of a Turkish Individual

Although whole human genome sequencing can be done with readily available technical and financial resources, the need for detailed analyses of genomes of certain populations still exists. Here we present, for the first time, sequencing and analysis of a Turkish human genome. We have performed 35x coverage using paired-end sequencing, where over 95% of sequencing reads are mapped to the reference genome covering more than 99% of the bases. The assembly of unmapped reads rendered 11,654 contigs, 2,168 of which did not reveal any homology to known sequences, resulting in ∼1 Mbp of unmapped sequence. Single nucleotide polymorphism (SNP) discovery resulted in 3,537,794 SNP calls with 29,184 SNPs identified in coding regions, where 106 were nonsense and 259 were categorized as having a high-impact effect. The homo/hetero zygosity (1,415,123∶2,122,671 or 1∶1.5) and transition/transversion ratios (2,383,204∶1,154,590 or 2.06∶1) were within expected limits. Of the identified SNPs, 480,396 were potentially novel with 2,925 in coding regions, including 48 nonsense and 95 high-impact SNPs. Functional analysis of novel high-impact SNPs revealed various interaction networks, notably involving hereditary and neurological disorders or diseases. Assembly results indicated 713,640 indels (1∶1.09 insertion/deletion ratio), ranging from −52 bp to 34 bp in length and causing about 180 codon insertion/deletions and 246 frame shifts. Using paired-end- and read-depth-based methods, we discovered 9,109 structural variants and compared our variant findings with other populations. Our results suggest that whole genome sequencing is a valuable tool for understanding variations in the human genome across different populations. Detailed analyses of genomes of diverse origins greatly benefits research in genetics and medicine and should be conducted on a larger scale.     

RATT: Rapid Annotation Transfer Tool

Second-generation sequencing technologies have made large-scale sequencing projects commonplace. However, making use of these datasets often requires gene function to be ascribed genome wide. Although tool development has kept pace with the changes in sequence production, for tasks such as mapping, de novo assembly or visualization, genome annotation remains a challenge. We have developed a method to rapidly provide accurate annotation for new genomes using previously annotated genomes as a reference. The method, implemented in a tool called RATT (Rapid Annotation Transfer Tool), transfers annotations from a high-quality reference to a new genome on the basis of conserved synteny. We demonstrate that a Mycobacterium tuberculosis genome or a single 2.5 Mb chromosome from a malaria parasite can be annotated in less than five minutes with only modest computational resources. RATT is available at http://ratt.sourceforge.net.     

Mining livestock genome datasets for an unconventional characterization of animal DNA viromes

Whole genome sequencing (WGS) datasets, usually generated for the investigation of the individual animal genome, can be used for additional mining of the fraction of sequencing reads that remains unmapped to the respective reference genome. A significant proportion of these reads contains viral DNA derived from viruses that infected the sequenced animals. In this study, we mined more than 480 billion sequencing reads derived from 1471 WGS datasets produced from cattle, pigs, chickens and rabbits. We identified 367 different viruses among which 14, 11, 12 and 1 might specifically infect the cattle, pig, chicken and rabbit, respectively. Some of them are ubiquitous, avirulent, highly or potentially damaging for both livestock and humans. Retrieved viral DNA information provided a first unconventional and opportunistic landscape of the livestock viromes that could be useful to understand the distribution of some viruses with potential deleterious impacts on the animal food production systems.     

An Efficient Algorithm for Mapping of Reads to a Genome Graph Using an Index Based on Hash Tables and Dynamic Programming

The problem of storage of the sequences of a number of closely related genomes and analysis of genome variations is considered. A genome graph with the structure of an acyclic directed graph is used to store matching sections of sequences and known variants. An algorithm for rapid mapping of reads to the genome graph is developed to align the individual nucleotide sequence fragments to the genome graph. The algorithm combines rapid searching using hash tables with the algorithm of dynamic programming and solves the problem of exponential growth in the number of paths on the graph. The implementation of the genome graph and the algorithm of the alignment of reads is developed. A comparison with the best-known programs with similar functionality is made.     

植物古基因组学研究进展

植物古基因组学是基因组学一个新兴分支,从现存物种中重建其祖先基因组,推断在古历史中导致形成现存物种的进化或物种形成事件。高通量测序技术的不断革新使测序读长更长、更准确,加快了植物参考基因组序列的组装进程,为古基因组学研究提供了大批量可靠的现存物种的基因组序列资源。全基因组复制(whole-genome duplication, WGD)亦称古多倍化,使植物基因组快速重组,丢失大量基因,增加结构变异,对植物进化极其重要。本文综述了植物基因组测序与组装研究进展、植物古基因组学的原理、植物基因组WGD事件以及植物祖先基因组进化场景,并对未来植物古基因组学研究进行了展望。     

A graph extension of the positional Burrows–Wheeler transform and its applications

We present a generalization of the positional Burrows–Wheeler transform, or PBWT, to genome graphs, which we call the gPBWT. A genome graph is a collapsed representation of a set of genomes described as a graph. In a genome graph, a haplotype corresponds to a restricted form of walk. The gPBWT is a compressible representation of a set of these graph-encoded haplotypes that allows for efficient subhaplotype match queries. We give efficient algorithms for gPBWT construction and query operations. As a demonstration, we use the gPBWT to quickly count the number of haplotypes consistent with random walks in a genome graph, and with the paths taken by mapped reads; results suggest that haplotype consistency information can be practically incorporated into graph-based read mappers. We estimate that with the gPBWT of the order of 100,000 diploid genomes, including all forms structural variation, could be stored and made searchable for haplotype queries using a single large compute node.     

Correcting base-assignment errors in repeat regions of shotgun assembly

Accurate base-assignment in repeat regions of a whole genome shotgun assembly is an unsolved problem. Since reads in repeat regions cannot be easily attributed to a unique location in the genome, current assemblers may place these reads arbitrarily. As a result, the base-assignment error rate in repeats is likely to be much higher than that in the rest of the genome. We developed an iterative algorithm, EULER-AIR, that is able to correct base-assignment errors in finished genome sequences in public databases. The Wolbachia genome is among the best finished genomes. Using this genome project as an example, we demonstrated that EULER-AIR can 1) discover and correct base-assignment errors, 2) provide accurate read assignments, 3) utilize finishing reads for accurate base-assignment, and 4) provide guidance for designing finishing experiments. In the genome of Wolbachia, EULER-AIR found 16 positions with ambiguous base-assignment and two positions with erroneous bases. Besides Wolbachia, many other genome sequencing projects have significantly fewer finishing reads and, hence, are likely to contain more base-assignment errors in repeats. We demonstrate that EULER-AIR is a software tool that can be used to find and correct base-assignment errors in a genome assembly project     

High-throughput Physical Mapping of Chromosomes using Automated in situ Hybridization

Projects to obtain whole-genome sequences for 10,000 vertebrate species¹ and for 5,000 insect and related arthropod species² are expected to take place over the next 5 years. For example, the sequencing of the genomes for 15 malaria mosquitospecies is currently being done using an Illumina platform^3,4. This Anopheles species cluster includes both vectors and non-vectors of malaria. When the genome assemblies become available, researchers will have the unique opportunity to perform comparative analysis for inferring evolutionary changes relevant to vector ability. However, it has proven difficult to use next-generation sequencing reads to generate high-quality de novo genome assemblies⁵. Moreover, the existing genome assemblies for Anopheles gambiae, although obtained using the Sanger method, are gapped or fragmented^4,6.Success of comparative genomic analyses will be limited if researchers deal with numerous sequencing contigs, rather than with chromosome-based genome assemblies. Fragmented, unmapped sequences create problems for genomic analyses because: (i) unidentified gaps cause incorrect or incomplete annotation of genomic sequences; (ii) unmapped sequences lead to confusion between paralogous genes and genes from different haplotypes; and (iii) the lack of chromosome assignment and orientation of the sequencing contigs does not allow for reconstructing rearrangement phylogeny and studying chromosome evolution. Developing high-resolution physical maps for species with newly sequenced genomes is a timely and cost-effective investment that will facilitate genome annotation, evolutionary analysis, and re-sequencing of individual genomes from natural populations^7,8.Here, we present innovative approaches to chromosome preparation, fluorescent in situ hybridization (FISH), and imaging that facilitate rapid development of physical maps. Using An. gambiae as an example, we demonstrate that the development of physical chromosome maps can potentially improve genome assemblies and, thus, the quality of genomic analyses. First, we use a high-pressure method to prepare polytene chromosome spreads. This method, originally developed for Drosophila⁹, allows the user to visualize more details on chromosomes than the regular squashing technique¹⁰. Second, a fully automated, front-end system for FISH is used for high-throughput physical genome mapping. The automated slide staining system runs multiple assays simultaneously and dramatically reduces hands-on time¹¹. Third, an automatic fluorescent imaging system, which includes a motorized slide stage, automatically scans and photographs labeled chromosomes after FISH¹². This system is especially useful for identifying and visualizing multiple chromosomal plates on the same slide. In addition, the scanning process captures a more uniform FISH result. Overall, the automated high-throughput physical mapping protocol is more efficient than a standard manual protocol.     

DHPC: a new tool to express genome structural features

The DHPC (DNA Hilbert-Peano curve) is a new tool for visualizing large-scale genome sequences by mapping sequences into a two-dimensional square. It utilizes the space-filling function of Hilbert-Peano mapping. By applying a Gauss smoothing technique and a user-defined color function, a large-scale genome sequence can be mapped into a two-dimensional color image. In the calculated DHPCs, many genome characteristics are revealed. In this article we introduce the method and show how DHPCs may be used to identify regions of different base composition. The power of the method is demonstrated by presenting multiple examples such as repeating sequences, degree of base bias, regions of homogeneity and their boundaries, and mark of annotated segments. We also present several genome curves generated by DHPC to demonstrate how DHPC can be used to find previously unidentified sequence features in these genomes.     

Castor bean organelle genome sequencing and worldwide genetic diversity analysis

Castor bean is an important oil-producing plant in the Euphorbiaceae family. Its high-quality oil contains up to 90% of the unusual fatty acid ricinoleate, which has many industrial and medical applications. Castor bean seeds also contain ricin, a highly toxic Type 2 ribosome-inactivating protein, which has gained relevance in recent years due to biosafety concerns. In order to gain knowledge on global genetic diversity in castor bean and to ultimately help the development of breeding and forensic tools, we carried out an extensive chloroplast sequence diversity analysis. Taking advantage of the recently published genome sequence of castor bean, we assembled the chloroplast and mitochondrion genomes extracting selected reads from the available whole genome shotgun reads. Using the chloroplast reference genome we used the methylation filtration technique to readily obtain draft genome sequences of 7 geographically and genetically diverse castor bean accessions. These sequence data were used to identify single nucleotide polymorphism markers and phylogenetic analysis resulted in the identification of two major clades that were not apparent in previous population genetic studies using genetic markers derived from nuclear DNA. Two distinct sub-clades could be defined within each major clade and large-scale genotyping of castor bean populations worldwide confirmed previously observed low levels of genetic diversity and showed a broad geographic distribution of each sub-clade.     

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.     

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 .     

Mapping reads on a genomic sequence: an algorithmic overview and a practical comparative analysis

Mapping short reads against a reference genome is classically the first step of many next-generation sequencing data analyses, and it should be as accurate as possible. Because of the large number of reads to handle, numerous sophisticated algorithms have been developped in the last 3 years to tackle this problem. In this article, we first review the underlying algorithms used in most of the existing mapping tools, and then we compare the performance of nine of these tools on a well controled benchmark built for this purpose. We built a set of reads that exist in single or multiple copies in a reference genome and for which there is no mismatch, and a set of reads with three mismatches. We considered as reference genome both the human genome and a concatenation of all complete bacterial genomes. On each dataset, we quantified the capacity of the different tools to retrieve all the occurrences of the reads in the reference genome. Special attention was paid to reads uniquely reported and to reads with multiple hits.     

Evaluating the effect of reference genome divergence on the analysis of empirical RADseq datasets

The advent of high‐throughput sequencing (HTS) has made genomic‐level analyses feasible for nonmodel organisms. A critical step of many HTS pipelines involves aligning reads to a reference genome to identify variants. Despite recent initiatives, only a fraction of species has publically available reference genomes. Therefore, a common practice is to align reads to the genome of an organism related to the target species; however, this could affect read alignment and bias genotyping. In this study, I conducted an experiment using empirical RADseq datasets generated for two species of salmonids (Actinopterygii; Teleostei; Salmonidae) to address these questions. There are currently reference genomes for six salmonids of varying phylogenetic distance. I aligned the RADseq data to all six genomes and identified variants with several different genotypers, which were then fed into population genetic analyses. Increasing phylogenetic distance between target species and reference genome reduced the proportion of reads that successfully aligned and mapping quality. Reference genome also influenced the number of SNPs that were generated and depth at those SNPs, although the affect varied by genotyper. Inferences of population structure were mixed: increasing reference genome divergence reduced estimates of differentiation but similar patterns of population relationships were found across scenarios. These findings reveal how the choice of reference genome can influence the output of bioinformatic pipelines. It also emphasizes the need to identify best practices and guidelines for the burgeoning field of biodiversity genomics.     

MaxSSmap: a GPU program for mapping divergent short reads to genomes with the maximum scoring subsequence

Background

Programs based on hash tables and Burrows-Wheeler are very fast for mapping short reads to genomes but have low accuracy in the presence of mismatches and gaps. Such reads can be aligned accurately with the Smith-Waterman algorithm but it can take hours and days to map millions of reads even for bacteria genomes.

Results

We introduce a GPU program called MaxSSmap with the aim of achieving comparable accuracy to Smith-Waterman but with faster runtimes. Similar to most programs MaxSSmap identifies a local region of the genome followed by exact alignment. Instead of using hash tables or Burrows-Wheeler in the first part, MaxSSmap calculates maximum scoring subsequence score between the read and disjoint fragments of the genome in parallel on a GPU and selects the highest scoring fragment for exact alignment. We evaluate MaxSSmap’s accuracy and runtime when mapping simulated Illumina E.coli and human chromosome one reads of different lengths and 10% to 30% mismatches with gaps to the E.coli genome and human chromosome one. We also demonstrate applications on real data by mapping ancient horse DNA reads to modern genomes and unmapped paired reads from NA12878 in 1000 genomes.

Conclusions

We show that MaxSSmap attains comparable high accuracy and low error to fast Smith-Waterman programs yet has much lower runtimes. We show that MaxSSmap can map reads rejected by BWA and NextGenMap with high accuracy and low error much faster than if Smith-Waterman were used. On short read lengths of 36 and 51 both MaxSSmap and Smith-Waterman have lower accuracy compared to at higher lengths. On real data MaxSSmap produces many alignments with high score and mapping quality that are not given by NextGenMap and BWA. The MaxSSmap source code in CUDA and OpenCL is freely available from http://www.cs.njit.edu/usman/MaxSSmap.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-969) contains supplementary material, which is available to authorized users.