Journal: Genome Biology

Article Title: Comprehensive identification of somatic nucleotide variants in human brain tissue

doi: 10.1186/s13059-021-02285-3

Figure Lengend Snippet: Overview of the mosaic SNV discovery and validation pipeline. a WGS or WES datasets were generated by six BSMN working groups using a commonly shared, homogenized DLPFC sample from a neurotypical individual and isogenic dural fibroblasts. Six different analytical methods initially were used to call mosaic SNVs. WGS data also was generated from sorted NeuN+ and NeuN− cells from DLPFC, cerebellum, and dura mater samples. Chromium 10X linked read sequencing data was generated from DLPFC and dural fibroblast samples. Single-cell WGS sequencing was conducted on twelve NeuN+ neurons from the DLPFC. These datasets were used to validate mosaic SNVs. b Overlap of putative mosaic SNV calls using different analytical approaches. Indicated are the numbers of mosaic SNV calls ( x -axis) and the numbers of mosaic SNV calls identified using different analytical approaches ( y -axis; circles with connecting lines indicate candidate SNVs identified by multiple approaches). c Candidate SNVs were subject to validation experiments using four complementary approaches. d Rationale of the empirical substitution error model applied to validate mosaic SNVs in PCR amplicon-based sequencing experiments. e An example of the empirical nucleotide error profiles encountered in a PCR amplicon-based sequencing experiment. Shown is the cumulative fraction of sites ( x -axis) and per site noise levels ( y -axis)

Article Snippet: We used haplotype information provided by 10X Genomics linked read sequencing data to eliminate false-positive mosaic SNV calls and to provide support for bona fide mosaic SNVs.

Techniques: Biomarker Discovery, Generated, Sequencing, Amplification

Journal: Genome Biology

Article Title: Comprehensive identification of somatic nucleotide variants in human brain tissue

doi: 10.1186/s13059-021-02285-3

Figure Lengend Snippet: Summary of validation results for 400 candidate mosaic SNVs. Vertical lines represent candidate mosaic SNVs. Shaded rectangles to the right of the figure provide the keys to interpret the shading presented for each candidate SNV. There was concordance in true-positive mosaic SNV calls (PASS; green rectangle at bottom of figure) in multiple datasets and secondary validation experiments. Chromium linked read haplotype phasing and single-cell sequencing datasets also were effective in supporting a subset of bona fide mosaic SNV calls. By comparison, the VAFs of false-positive calls (red rectangle) are inconsistent across different datasets and often occur within or near insertion/deletion (indel) mutations, short tandem repeat sequences (STRs), homopolymeric nucleotide stretches, or copy number variants (CNVs). Importantly, the panel of normal (PON) filter, but not the comparison to WGS data from a control sample (i.e., to NA12878), was highly effective at identifying contaminating false-positive SNV calls (orange rectangle) and germline SNPs (gray rectangle). We lacked sufficient data to evaluate a subset of candidate SNVs (purple rectangle, NED—not enough data). The two green triangles at the top of the figure denote mosaic SNVs that validation experiments deemed to be false-positive calls; however, cell lineage analyses demonstrated that they are likely bona fide mosaic SNVs (see text and Fig. )

Article Snippet: We used haplotype information provided by 10X Genomics linked read sequencing data to eliminate false-positive mosaic SNV calls and to provide support for bona fide mosaic SNVs.

Techniques: Biomarker Discovery, Sequencing, Comparison, Control

Journal: Brain Sciences

Article Title: Searching the Dark Genome for Alzheimer’s Disease Risk Variants

doi: 10.3390/brainsci11030332

Figure Lengend Snippet: A simplified, schematic diagram comparing short read and long read mapping to reference genome for long and short repeats. Sequencing reads are indicated by partial arrows. Grey rectangles represent target sequence. Flanking sequence is represented by green rectangles. Reference genome used to align the reads is indicated in blue. Reads poorly or not aligned to the reference genome are indicated by X. Success in mapping to reference genome indicated by a tick.

Article Snippet: An alternative approach to long read whole genome sequencing is offered by 10X Genomics.

Techniques: Sequencing

Journal: Genome Biology

Article Title: Systematic analysis of dark and camouflaged genes reveals disease-relevant genes hiding in plain sight

doi: 10.1186/s13059-019-1707-2

Figure Lengend Snippet: Genomic regions may be “dark” by depth or mapping quality, many of which are “camouflaged”. Large, complex genomes are known to contain “dark” regions where standard high-throughput short-read sequencing technologies cannot be adequately assembled or aligned. We split these dark regions into two types: (1) dark because of low depth and (2) dark because of low mapping quality (MAPQ), which are mostly “camouflaged”. a HLA-DRB5 encodes a Major Histocompatibility Complex protein that plays an important role in immune response and has been associated with several diseases, including Alzheimer’s disease. It is well known to be dark (low depth); specifically, when performing whole-genome sequencing using standard short-read sequencing technologies, an insufficient number of reads align, preventing variant callers from assessing mutations. We calculated sequencing depth across HLA-DRB5 for ten male samples from the Alzheimer’s Disease Sequencing Project (ADSP) that were sequenced using standard Illumina whole-genome sequencing with 100-nucleotide read lengths. Approximately 63.5% (49.6% of coding sequence) of HLA-DRB5 is dark by depth (≤ 5 aligned reads; indicated by red lines). b HSPA1A is a heat-shock protein from the 70-kilodalton (kDa) heat-shock protein family and plays an important role in stabilizing proteins against aggregation. HSPA1A is dark because of low mapping quality (MAPQ < 10 for ≥ 90% of reads at a given position). Approximately 41.1% (53.0% coding sequence) of HSPA1A is dark by mapping quality (indicated by red line). Dark gray bars indicate sequencing reads with a relatively high mapping quality, whereas white bars indicate reads with a low mapping quality (MAPQ = 0). c Many genomic regions that are dark because of mapping quality arise because they have been duplicated in the genome, which we term “camouflaged” (or “camo genes”). When confronted with a read that aligns equally well to more than one location, standard sequence aligners randomly assign the read to one location and give it a low mapping quality. Thus, it is unclear from which gene any of the reads indicated by white bars originated from. HSPA1A and HSPA1B are clear examples of camouflaged genes arising from a tandem duplication. The two genes are approximately 14 kb apart and approximately 50% of the genes are identical

Article Snippet: Linked- and long-read sequencing technologies, such as those from 10x Genomics, Oxford Nanopore Technologies (ONT), and Pacific Biosciences (PacBio), have the potential to address many camouflaged regions because these technologies have median read lengths measured in thousands of nucleotides, rather than only 100–300 nucleotides from standard short-read sequencing technologies (e.g., Illumina).

Techniques: High Throughput Screening Assay, Sequencing, Immunopeptidomics, Variant Assay

Journal: Genome Biology

Article Title: Systematic analysis of dark and camouflaged genes reveals disease-relevant genes hiding in plain sight

doi: 10.1186/s13059-019-1707-2

Figure Lengend Snippet: Dark and camouflaged regions vary by genome build. We identified dark and camouflaged regions throughout the genome for three different builds, including GRCh37, GRCh38, and GRCh38+alt, across five different sequencing technologies (or read lengths for Illumina). Specifically, we measured dark regions for Illumina based on 100-nucleotide read lengths, Illumina based on 250-nucleotide read lengths, 10x Genomics, PacBio, and Oxford Nanopore Technologies (ONT). Here, the counts for dark and camouflaged regions are combined. We found that the number of dark regions and nucleotides, both within gene bodies (represented as GB in the table) and outside gene bodies, varies dramatically by build and technology. Overall, each technology has its respective strengths. GRCh38 including alternate contigs has > 3x more dark nucleotides than GRCh37, and more than 2x more dark regions. Results presented throughout the manuscript are based on GRCh38 (in gray)

Article Snippet: Linked- and long-read sequencing technologies, such as those from 10x Genomics, Oxford Nanopore Technologies (ONT), and Pacific Biosciences (PacBio), have the potential to address many camouflaged regions because these technologies have median read lengths measured in thousands of nucleotides, rather than only 100–300 nucleotides from standard short-read sequencing technologies (e.g., Illumina).

Techniques: Sequencing

Journal: Genome Biology

Article Title: Systematic analysis of dark and camouflaged genes reveals disease-relevant genes hiding in plain sight

doi: 10.1186/s13059-019-1707-2

Figure Lengend Snippet: Dark coding regions occur throughout the genome and are largely resolved with long-read sequencing technologies. We identified 2855 dark coding (CDS) regions in 748 protein-coding genes that were dark by either depth or mapping quality (Additional file : Table S1; Additional file : Table S2). We identified 117 (15.6%) of the 748 protein-coding genes were 100% dark in CDS regions, 402 (53.7%) were at least 25% dark in CDS regions, and 592 (79.1%) were at least 5% dark in CDS regions (Additional file : Table S1). a We mapped all protein-coding gene bodies with a dark coding exon to the genome to visualize their genomic location and are generally spread throughout. There are several tight clusters of dark CDS regions on chromosomes 1, 9, 10, and Y, however. b We assessed how well increasing read lengths would resolve dark regions by assessing samples sequenced with Illumina whole-genome sequencing using 250-nucleotide read lengths, as well as long-read technologies 10x Genomics, Oxford Nanopore Technologies (ONT), and Pacific Biosciences (PacBio). Data from the samples sequenced using 250-nucleotide Illumina read lengths reduced the area under the curve (AUC) by 12.1% in CDS regions. Comparing long-read sequencing technologies to the standard Illumina 100-nucleotide read lengths, 10x Genomics, PacBio, and ONT reduced the area under the curve for CDS regions by approximately 49.5%, 64.4%, and 90.4%, respectively. The AUC for each technology is scaled in reference to Illumina sequencing based on 100-nucleotide read lengths (i.e., AUC for Illumina 100-nucleotide read lengths = 1). In contrast to overall results, PacBio outperformed 10x Genomics when looking only at CDS regions (see text). Most analyses focused on genes where at least 5% of the CDS nucleotides are dark, indicated by the dashed line. c , d We also calculated the raw number of dark nucleotides for each technology in GRCh38, genome wide, in full gene bodies, and in CDS regions

Article Snippet: Linked- and long-read sequencing technologies, such as those from 10x Genomics, Oxford Nanopore Technologies (ONT), and Pacific Biosciences (PacBio), have the potential to address many camouflaged regions because these technologies have median read lengths measured in thousands of nucleotides, rather than only 100–300 nucleotides from standard short-read sequencing technologies (e.g., Illumina).

Techniques: Sequencing, Illumina Sequencing, Genome Wide

Journal: Genome Biology

Article Title: Systematic analysis of dark and camouflaged genes reveals disease-relevant genes hiding in plain sight

doi: 10.1186/s13059-019-1707-2

Figure Lengend Snippet: Camouflaged genes are consistently dark in gnomAD, but dark-by-depth genes may be sample or dataset specific. Many dark genes are specifically camouflaged (Additional file : Table S12; Additional file : Table S13), but many are dark by depth; we found that camouflaged regions in the ADSP are consistently dark in the gnomAD consortium data ( http://gnomad.broadinstitute.org/ ) . Dark-by-depth regions may be more variable between samples and datasets, however, suggesting these regions may be sensitive to specific aspects of whole-genome sequencing (e.g., library preparation) or downstream analyses. a SMN1 and SMN2 are camouflaged by each other (only SMN1 shown). Both genes contribute to spinal muscular atrophy and have been implicated in ALS. b HSPA1A and HSPA1B are also camouflaged by each other (only HSPA1A shown). The heat-shock protein family has been implicated in ALS. c NEB (9.5% dark CDS) is a special case that is camouflaged by itself. NEB is associated with 24 diseases in the HGMD, including nemaline myopathy, a hereditary neuromuscular disorder. NEB is a large gene; thus, 9.5% dark CDS translates to 2424 protein-coding bases. d CR1 is a top Alzheimer’s disease gene that plays a critical role in the complement cascade as a receptor for the C3b and C4b complement components, and potentially helps clear amyloid-beta (Aβ) [ – ]. CR1 is also camouflaged by itself, where the repeated region includes the extracellular C3b and C4b binding domain. The number of repeats and density of certain isoforms have been associated with Alzheimer’s disease [ , – ]. e HLA-DRB5 is dark by depth in the ADSP and gnomAD data. HLA-DRB5 has been implicated in several diseases, including Alzheimer’s disease. f RPGR is likewise dark in ADSP and gnomAD and is associated with several eye diseases, including retinitis pigmentosa and cone-rod dystrophy. g ARX is dark-by-depth, but varies by sample or cohort, as approximately 70% of gnomAD samples are not strictly dark by depth. ARX is associated with diseases including early infantile epileptic encephalopathy 1 (EIEE1) and Partington syndrome. h Similarly, TBX1 is not strictly dark by depth in approximately 70% of gnomAD samples. The Y axes for figures a – f indicate median coverage in gnomAD (blue = exomes; green = genomes), whereas the Y axes in g , h represent the proportion of gnomAD samples that have > 5x coverage. Dark and camouflaged regions, as well as the percentage of each gene’s CDS region that is dark, are indicated by red lines. Dark regions in exome data are either similar or more pronounced than what we observed in whole-genome data, highlighting that dark and camouflaged regions are generally magnified in whole-exome data. For interest, we also discovered that APOE —the top genetic risk for Alzheimer’s disease [ – ]—is approximately 6% dark CDS (by depth) for certain ADSP samples with whole-genome sequencing, and the same region is dark in gnomAD whole-exome data (Additional file : Figure S11)

Article Snippet: Linked- and long-read sequencing technologies, such as those from 10x Genomics, Oxford Nanopore Technologies (ONT), and Pacific Biosciences (PacBio), have the potential to address many camouflaged regions because these technologies have median read lengths measured in thousands of nucleotides, rather than only 100–300 nucleotides from standard short-read sequencing technologies (e.g., Illumina).

Techniques: Sequencing, Binding Assay

Journal: Genome Biology

Article Title: Systematic analysis of dark and camouflaged genes reveals disease-relevant genes hiding in plain sight

doi: 10.1186/s13059-019-1707-2

Figure Lengend Snippet: Long-read technologies resolve many camouflaged regions, with variable success. We found that ONT’s long-read technology appeared to resolve all camouflaged regions well with the high sequencing depth. PacBio performed similarly well, and 10x Genomics performs well under certain circumstances. a SMN1 and SMN2 were 94.6% and 88.0% camouflaged CDS, respectively, based on standard Illumina sequencing with 100-nucleotide read lengths (illuminaRL100). Both genes were 0% camouflaged CDS for 10x Genomics, PacBio, and ONT data. 10x Genomics and ONT perform particularly well in these genes, with consistently high mapping coverage. b HSPA1A and HSPA1B were 53.0% and 51.5% camouflaged CDS, respectively, based on illuminaRL100 data. Both genes were 0% camouflaged CDS based on ONT and PacBio data and were 45.8% and 51.8% camouflaged CDS based on 10x Genomics data. In contrast to the results for SMN1 and SMN2 , 10x Genomics was unable to resolve the HSPA1A and HSPA1B camouflaged regions. c CR1 was 26.0% camouflaged CDS based on illuminaRL100. 10x Genomics did not improve coverage for CR1 ; the region remained 26.4% camouflaged CDS. Both ONT and PacBio were 0% camouflaged CDS. While both PacBio and ONT were able to fill the camouflaged region, coverage dropped throughout the region, particularly for PacBio. The duplicated region is indicated by blue bars, where white lines indicate regions that have diverged sufficiently for short-reads to align uniquely. Regions were visualized with IGV. Reads with a MAPQ < 10 were filtered, and insertions, deletions, and mismatches are not shown

Article Snippet: Linked- and long-read sequencing technologies, such as those from 10x Genomics, Oxford Nanopore Technologies (ONT), and Pacific Biosciences (PacBio), have the potential to address many camouflaged regions because these technologies have median read lengths measured in thousands of nucleotides, rather than only 100–300 nucleotides from standard short-read sequencing technologies (e.g., Illumina).

Techniques: Sequencing, Illumina Sequencing

Journal: Genome Biology

Article Title: Systematic analysis of dark and camouflaged genes reveals disease-relevant genes hiding in plain sight

doi: 10.1186/s13059-019-1707-2

Figure Lengend Snippet: Many camouflaged regions can be rescued, including CR1 , even in standard short-read sequencing data. Many large-scale whole-genome or whole-exome sequencing projects exist, covering tens of thousands of individuals. All of these datasets are affected by dark and camouflaged regions that may harbor mutations that either drive or modify disease in patients. Ideally, all samples would be re-sequenced using the latest technologies over time, but financial resources and biological samples are limited, making it essential to maximize the utility of existing data. We developed a method to rescue mutations in most camouflaged regions, including for standard short-read sequencing data. When confronted with a sequencing read that aligns to two or more regions equally well (with high confidence), most aligners (e.g., BWA [ – ]) will randomly assign the read to one of the regions with a low mapping quality (e.g., MAPQ = 0 for BWA). a Because the reads are already aligned to one of the regions, we can use the following steps to rescue mutations in most camouflaged regions: (1) extract reads from camouflaged regions, (2) mask all highly similar regions in the reference genome, except one, and re-align the extracted reads, (3) call mutations using standard methods (adjusting for ploidy), and (4) determine precise location using targeted sequencing (e.g., long-range PCR combined with Sanger, or targeted long-read sequencing ). Without competing camouflaged regions to confuse the aligner, the aligner will assign a high mapping quality, allowing variant callers to behave normally. b Exons 10, 18, and 26 in CR1 are identical, according to the reference genome. Standard aligners will randomly scatter reads matching that sequence across these exons and assign a low mapping quality (e.g., MAPQ = 0 for BWA; indicated as hollow reads). Red lines indicate an individual’s mutation that exists in one of these exons, but reads containing this mutation also get scattered and assigned a low mapping quality. c By masking exons 18 and 26, we can align all of these reads to exon 10 with high mapping qualities to determine whether a mutation exists. We cannot determine at this stage which of the three exons the mutation is actually located in, but researchers can test association with a given disease to determine whether the mutation is worth further investigation. d As a proof of principle, we rescued approximately 4214 exonic variants in the ADSP (TiTv = 2.26) using our method, including a frameshift mutation in CR1 (MAF = 0.00019) that is found in five cases and zero controls (three representative samples shown). The frameshift results in a stop codon shortly downstream. The ADSP is not large enough to formally assess association between the CR1 frameshift and Alzheimer’s disease, but we believe the mutation merits follow-up studies given its location ( CR1 binding domain) and CR1 ’s strong association with disease

Article Snippet: Linked- and long-read sequencing technologies, such as those from 10x Genomics, Oxford Nanopore Technologies (ONT), and Pacific Biosciences (PacBio), have the potential to address many camouflaged regions because these technologies have median read lengths measured in thousands of nucleotides, rather than only 100–300 nucleotides from standard short-read sequencing technologies (e.g., Illumina).

Techniques: Sequencing, Long Range PCR, Variant Assay, Mutagenesis, Binding Assay