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

Abstract

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. Heredity (2015) 114, 494-501; doi:10.1038/hdy.2014.85; published online 1 October 2014 INTRODUCTION Next-generation sequencing and whole-genome re-sequencing is nowadays commonly used to identify genomic variants that underlie phenotypic variations, genetic diseases, adaptation or speciation in natural populations. Typically, the reads are mapped against a reference genome, and the genotypes (that is, single-nucleotide polymorphism (SNP) and structural variant calls) are based on these mapped reads (Altshuler et al., 2010;Nielsen et al., 2011). In addition to universal caveats regarding unknown insertions and/or genomic contamination, which can be overlooked in pure mapping approaches, non-model organisms may suffer from the poor quality of the nuclear reference genome and incomplete symbiont or organellar genomes. Moreover, mapping is constrained by the level of divergence between the reads and the available reference sequence (Sousa and Hey, 2013). The resulting ascertainment bias could be problematic, especially when studying adaptation or speciation processes, as genomic regions of interest are expected to display important levels of divergence. These different issues produce a non-negligible fraction of unmapped reads, whose sequences are generally disregarded in favor of the mapped reads in the subsequent steps of the analysis, despite potentially containing useful information. This study offers one strategy for mining the unmapped reads in order to extract the relevant biological knowledge, leading to advice and recommendations for other re-sequencing projects.We investigated this question in the context of a large-scale resequencing project on the pea aphid species complex. The pea aphid

MetaMap: An atlas of metatranscriptomic reads in human disease-related RNA-seq data

“…human) transcriptomics such as differential gene expression or alternative splicing analysis [9,10]. Reads that do not map onto the human genome are considered noise or contamination and therefore generally ignored [11,12] (collectively about 9% of total reads, Fig. 1).…”

Haplotype-centered mapping for improved alignments and genetic association studies

“…Unmapped reads would not be a problem if these reads were all redundant, consisted of low quality bases, or originated from regions of little biological relevance. However, the majority of unmapped reads are high quality, contain relevant biological information [10], and there are many genomic regions with no mapped reads (e.g., see Figure 1). Reads are unmappable because they are too short to have high homology to only a single location in the genome, reads have a high error rate, the reference and subject genomes are substantially different, and insertions and deletions cause mapping problems.…”

Exploring the unmapped DNA and RNA reads in a songbird genome

“…Such information can be e.g. pathogens, symbionts or sequences/genes missing in the reference genome [1][2][3][4][5].…”