BAM Files Research Articles

B-234 The Quality in NGS: Do You Know How Much We Lost?

Abstract Background Next generation sequencing (NGS) is a new high throughput approach for genomic sequencing and variations detection. The consumption of NGS is numerous, such as times, money and human resource, therefore, some hospitals provided the service but delivered the specimen to other laboratories. Hence, we aimed to establish a procedure to monitor the quality and integrity of report. Method Here we used one clinical case to elucidate the whole process of raw sequence data analysis by free resource, a website usegalaxy.org and the IGV visualization software, with flowchart and quality control (QC) items. A delivered case will be an example to show how to monitor the QC. Results The flow chart showed the whole steps of analysis of NGS, including evaluation genetic variants in the report from individual laboratory. We processed the FASTQ file and analyzed the quality with three parameters. An overall picture of bases showed the phred score (QV) less than 28. Distribution of sequence lengths over all sequences highlighted a prominent peak at 110–119 bp. The mean QV of reads was around 26 with low quality (QV less than 20) in some reads. We then filtered the FASTQ file and the reads with length less than 45 bp or greater than 150 bp and QV less than 20 were filtered out. The data of the BAM file mapped to the designed panel were output. There were 8252 genome regions designed over 23 chromosomes (chr). A total number of reads was 1 085 653 with the average 131.6 reads (range: 0–31034; quartiles, IQ25: 1; IQ50: 27; IQ75: 152). 7746 of designed genome regions (93.9%) had reads less than 500 (quartiles, IQ25: 0; IQ50: 20; IQ75: 118). We further used IGV to check one of mutations which showed KRAS G12V in the report. KRAS G12V (chr.12: 25245350) was read 25 times with the variant allele frequency (C to A) of 24%. Conclusion We showed how to monitor the NGS quality by free resource. It is important to ensure high-quality data after we received the individual laboratory reports. That may help increase the quality of patient care and clinical service.

QuickBAM: a parallelized BAM file access API for high-throughput sequence analysis informatics.

In time-critical clinical settings, such as precision medicine, genomic data needs to be processed as fast as possible to arrive at data-informed treatment decisions in a timely fashion. While sequencing throughput has dramatically increased over the past decade, bioinformatics analysis throughput has not been able to keep up with the pace of computer hardware improvement, and consequently has now turned into the primary bottleneck. Modern computer hardware today is capable of much higher performance than current genomic informatics algorithms can typically utilize, therefore presenting opportunities for significant improvement of performance. Accessing the raw sequencing data from BAM files, e.g. is a necessary and time-consuming step in nearly all sequence analysis tools, however existing programming libraries for BAM access do not take full advantage of the parallel input/output capabilities of storage devices. In an effort to stimulate the development of a new generation of faster sequence analysis tools, we developed quickBAM, a software library to accelerate sequencing data access by exploiting the parallelism in commodity storage hardware currently widely available. We demonstrate that analysis software ported to quickBAM consistently outperforms their current versions, in some cases finishing an analysis in under 3 min while the original version took 1.5 h, using the same storage solution. Open source and freely available at https://gitlab.com/yiq/quickbam/, we envision that quickBAM will enable a new generation of high-performance informatics tools, either directly boosting their performance if they are currently data-access bottlenecked, or allow data-access to keep up with further optimizations in algorithms and compute techniques.

A robust and accurate method to detect MET Exon 14–skipping mutation in pan-cancer samples using an NGS-based RNAseq approach.

e20575 Background: Met exon 14 skipping mutation(METex14) occurs in multiple cancer species, mainly in non-small cell lung cancer (NSCLC, 3~4%) and pulmonary sarcomatoid carcinoma (PSC, 22%). Several drugs expressed curative effect in previously treated patients who harboring METex14, such as savolitinib approved by NMPA, capmatinib and tepotinib licenced by FDA. Developing a quick and precise method which detect METex14 and help patients benefit from clinical drug treatment is a desiderative issue currently. Previous researches suggest that transcriptome-based RNA-seq method was superior to DNA-based NGS (exome, panel) and PCR method. Here We introduce a fast and accurate method to detect METex14 alternation in RNAseq data. Methods: We sequenced NGS-based Ribo-zero RNAseq (pair-end, 150bp×2) and METex14 ddPCR on 99 pan-cancer samples, of which 38 lung carcinoma (38%), 8 renal carcinoma (8%), 7 pancreatic carcinoma (7%). In 38 lung carcinoma, 35 NSCLC samples were consist by 29 lung adenocarcinoma (LUAD, 76%), 4 lung squamous carcinoma (LUSC, 10%) and 2 PSC (5%). Begin of the study, data quality control applied by Fastp and aligned to human hg19 genome by STAR subsequently. Next, MET exon14 related candidate soft-clipping reads were extracted from BAM files and translated to fastq file. Then we remapped the translated fastq file to hg19 genome with Blat using parameters ‘stepSize = 5, -repMatch = 2253, -minScore = 20, -minIdentity = 90’. Next, supporting reads were sequentially retained by: single end length longer than 18bp; block mapped reads of MET gene; the pair end reads mapped to genome if one end fall in the last 3bp of MET intron 13 and another end fall in at least initial 3bp of MET exon 15. Pair or single end support junction read, distance of junction site to reads end and site coverage were enrolled in a bayesian model to calculate METex14 confidence score (BMMETs). Threshold of our model was defined by the cut-point of max youden-index and 16 samples were validated METex14 by ddPCR. Finally, we would predict a sample with METex14 when score > = 0.752. Results: Our method reported 15 TP samples, 1 FP sample and 1 FN samples (AUC 0.97, sensitivity 0.88, specificity 0.98) while STAR-fusion detected 14 TR samples and 6 FP sample and 2 FN samples (AUC 0.715, sensitivity 0.64, specificity 0.73). BMMETs FP sample scored approximately 0.739 that close to the threshold. Conclusions: Strategy of screen reliable METex14 supporting reads and using bayesian model to calculate a METex14 confidence score could robustly and efficiently predict METex14 event in NGS-based RNAseq data.

Nanoblot: an R-package for visualization of RNA isoforms from long-read RNA-sequencing data.

RT-PCR and northern blots have long been used to study RNA isoforms usage for single genes. Recent advancements in long-read sequencing have yielded unprecedented information about the usage and abundance of these RNA isoforms. However, visualization of long-read sequencing data remains challenging due to the high information density. To alleviate these issues, we have developed NanoBlot, an open-source R-package that generates northern blot and RT-PCR-like images from long-read sequencing data. NanoBlot requires aligned, positionally sorted and indexed BAM files. Plotting is based around ggplot2 and is easily customizable. Advantages of NanoBlot include a robust system for designing probes to visualize isoforms including excluding reads based on the presence or absence of a specified region, an elegant solution to representing isoforms with continuous variations in length, and the ability to overlay multiple genes in the same plot using different colors. We present examples of nanoblots compared to actual northern blot data. In addition to traditional gel-like images, the NanoBlot package can also output other visualizations such as violin plots and 3'-RACE-like plots focused on 3'-end isoforms visualization. The use of the NanoBlot package should provide a simple answer to some of the challenges of visualizing long-read RNA-sequencing data.

Identification and characterization of the largest deletion in the PCCA gene causing severe acute early-onset form of propionic acidemia.

Whole-exome sequencing (WES) is an excellent method for the diagnosis of diseases of uncertain or heterogeneous genetic origin. However, it has limitations for detecting structural variations such as InDels, which the bioinformatics analyzers must be aware of. This study aimed at using WES to evaluate the genetic cause of the metabolic crisis in a 3-day-old neonate admitted to the neonatal intensive care unit (NICU) and deceased after a few days. Tandem mass spectrometry (MS/MS) showed a significant increase in propionyl carnitine (C3), proposing methylmalonic acidemia (MMA) or propionic acidemia (PA). WES demonstrated a homozygous missense variant in exon 4 of the BTD gene (NM_000060.4(BTD):c.1330G > C), responsible for partial biotinidase deficiency. Segregation analysis of the BTD variant revealed the homozygous status of the asymptomatic mother. Furthermore, observation of the bam file, around genes responsible for PA or MMA, by Integrative Genomics Viewer (IGV) software displayed a homozygous large deletion in thePCCAgene. Comprehensive confirmatory studies identified and segregated a novel outframe deletion of 217,877bp length, "NG_008768.1:g.185211_403087delinsTA", extended from intron 11 to 21 of the PCCA, inducing a premature termination codon and activation of nonsense-mediated mRNA decay (NMD). Homology modeling of the mutant PCCA demonstrated eliminating the protein's active site and critical functional domains. Thereupon, this novel variant is suggested as the largest deletion in the PCCA gene, causing an acute early-onset PA. These results could expand the PCCA variants spectrum, and improve the existing knowledge on the molecular basis of PA, as well as provide new evidence of pathogenicity of the variant (NM_000060.4(BTD):c.1330G > C.

Fast all versus all genotype comparison using DNA/RNA sequencing data: method and workflow

BackgroundMassively parallel sequencing includes many liquid handling steps which introduce the possibility of sample swaps, mixing, and duplication. The unique profile of inherited variants in human genomes allows for comparison of sample identity using sequence data. A comparison of all samples vs. each other (all vs. all) provides both identification of mismatched samples and the possibility of resolving swapped samples. However, all vs. all comparison complexity grows as the square of the number of samples, so efficiency becomes essential.ResultsWe have developed a tool for fast all vs. all genotype comparison using low level bitwise operations built into the Perl programming language. Importantly, we have also developed a complete workflow allowing users to start with either raw FASTQ sequence files, aligned BAM files, or genotype VCF files and automatically generate comparison metrics and summary plots. The tool is freely available at https://github.com/teerjk/TimeAttackGenComp/.ConclusionsA fast and easy to use method for genotype comparison as described here is an important tool to ensure high quality and robust results in sequencing studies.

DNAfusion: an R/Bioconductor package for increased sensitivity of detecting gene fusions in liquid biopsies

BackgroundEML4-ALK gene fusions are oncogenic drivers in non-small cell lung cancer (NSCLC), and liquid biopsies containing EML4-ALK fragments can be used to study tumor dynamics using next-generation sequencing (NGS). However, the sensitivity of EML4-ALK detection varies between pipelines and analysis tools.ResultsWe developed an R/Bioconductor package, DNAfusion, which can be applied to BAM files generated by commercially available NGS pipelines, such as AVENIO. Forty-eight blood samples from a training cohort consisting of 41 stage IV EML4-ALK-positive NSCLC patients and seven healthy controls were used to develop DNAfusion. DNAfusion detected EML4-ALK in significantly more samples (sensitivity = 61.0%) compared to AVENIO (sensitivity = 36.6%). The newly identified EML4-ALK-positive patients were verified using droplet digital PCR. DNAfusion was subsequently validated in a blinded validation cohort comprising 24 EML4-ALK-positive and 24 EML4-ALK-negative stage IV NSCLC patients. DNAfusion detected significantly more EML4-ALK individuals in the validation cohort (sensitivity = 62.5%) compared to AVENIO (sensitivity = 29.2%). DNAfusion demonstrated a specificity of 100% in both the training and validation cohorts.ConclusionHere we present DNAfusion, which increases the sensitivity of EML4-ALK detection in liquid biopsies and can be implemented downstream of commercially available NGS pipelines. The simplistic method of operating the R package makes it easy to implement in the clinical setting, enabling wider expansion of NGS-based diagnostics.

Analysis of Genetic Ancestry from NGS Data Using EthSEQ

Next-generation sequencing (NGS) is widely utilized both in translational cancer genomics studies and in the setting of precision medicine. Identification and stratification of an individual's ancestry is fundamental for the correct interpretation of genetic and genomic profiling. EthSEQ provides an easy and effective computational workflow to determine the ancestry of individuals, exploiting single nucleotide polymorphism genotypes computed from NGS data of whole-exome and targeted sequencing experiments. Genotypes are determined by EthSEQ from sequencing alignment files (BAM files) or can be provided as input in Variant Call Format (VCF) or CoreArray Genomic Data Structure (GDS) format. Ancestry is determined and assigned to individuals by EthSEQ exploiting a reference model and a standard or multi-step refinement approach based on Principal Component Analysis (PCA). A complete and detailed set of textual and graphical output files are generated by EthSEQ as result. EthSEQ is easy to use and can be integrated into any NGS-based processing pipeline also exploiting multi-core capabilities. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Perform ancestry analysis using a pre-computed reference model Alternate Protocol: Perform ancestry analysis using a user-specified GDS file as reference model Basic Protocol 2: Perform ancestry analysis using multi-step refinement Support Protocol 1: Create a reference model from multiple VCF genotype data files Support Protocol 2: Create VCF genotype data files from a BAM file.

SCExecute: custom cell barcode-stratified analyses of scRNA-seq data.

In single-cell RNA-sequencing (scRNA-seq) data, stratification of sequencing reads by cellular barcode is necessary to study cell-specific features. However, apart from gene expression, the analyses of cell-specific features are not sufficiently supported by available tools designed for high-throughput sequencing data. We introduce SCExecute, which executes a user-provided command on barcode-stratified, extracted on-the-fly, single-cell binary alignment map (scBAM) files. SCExecute extracts the alignments with each cell barcode from aligned, pooled single-cell sequencing data. Simple commands, monolithic programs, multi-command shell scripts or complex shell-based pipelines are then executed on each scBAM file. scBAM files can be restricted to specific barcodes and/or genomic regions of interest. We demonstrate SCExecute with two popular variant callers-GATK and Strelka2-executed in shell-scripts together with commands for BAM file manipulation and variant filtering, to detect single-cell-specific expressed single nucleotide variants from droplet scRNA-seq data (10X Genomics Chromium System).In conclusion, SCExecute facilitates custom cell-level analyses on barcoded scRNA-seq data using currently available tools and provides an effective solution for studying low (cellular) frequency transcriptome features. SCExecute is implemented in Python3 using the Pysam package and distributed for Linux, MacOS and Python environments from https://horvathlab.github.io/NGS/SCExecute. Supplementary data are available at Bioinformatics online.

RF33 | PSAT69 A Combined Candidate Gene/Whole Exome Sequencing Approach Permits a Rapid Genetic Diagnosis for >81% Individuals with Primary Adrenal Insufficiency.

Abstract Introduction Mutations in MC2R causing familial glucocorticoid deficiency (FGD), a rare form of primary, isolated adrenal insufficiency, were first published in 1993, discovered by candidate gene sequencing (CGS). Through advances in genetic techniques from homozygosity mapping to whole exome sequencing (WES) we have linked a further five genes to adrenal insufficiency and, in conjunction with other groups, this has expanded the number of monogenic causes for isolated or syndromic adrenal insufficiency to >25. Patients and Methods Over the last 30 years over 400 individuals with suspected FGD, from 31 different countries, have been referred to our centre for genetic testing, ranging in age from neonates to individuals in their eighties. All cases had low or undetectable serum cortisol usually paired with an elevated plasma ACTH level. Our strategy for sequencing has evolved over time and we now routinely sequence the small, frequently mutated, candidate genes by Sanger sequencing (MC2R, MRAP, STAR and CYP11A1) before proceeding to WES as the most cost-effective route to a genetic diagnosis. In total, 369of the 400 individuals have been fully characterised either by CGS or WES. For CGS, sequences were analysed by alignment to reference sequences using BioEdit software and for WES variant call files were initially analysed using Ingenuity Variant Analysis package and/or examination of BAM files, using the Integrative Genomics Viewer, to detect exonic deletions. Rare, synonymous or predicted benign variants were further tested by an in vitro splicing assay using the pET01 vector (MoBiTec) Results In 308/ 369 individuals we found a definitive diagnosis in a gene known to be causal for adrenal insufficiency, giving a success rate of >81%, and identified 15 novel mutations. The aetiologies of diagnosed cases were as follows; MC2R (22%), MRAP (17%), NNT (15%), STAR (9%), CYP11A1 (7%), with the remaining 30% due to a further 13 genes. Founder effects were clear for variants in some genes/populations, whereas in others hotspot mutations were present in multiple ethnicities. Examples include the previously described S74I in MC2R and rs6161 in CYP11A1 in the UK population, P24Rfs*4 in MCM4 in Ireland, R188C in STAR in Canada, and newly discovered associations with T731= in NNT in Sudan and R222Q in SGPL1 in Saudi Arabia. Whereas MRAP splice mutations commonly seen at the exon 3/intron 3 junction were present in individuals from many countries. The work has also highlighted a number of causal synonymous and predicted benign variants that result in splicing defects. Conclusion The use of CGS/WES now permits a rapid genetic diagnosis for >83% individuals with PAI improving tailored patient management. For the remaining patients it is unclear whether they have unconventional mutations in known genes or if there are further gene defects to be discovered. Presentation: Saturday, June 11, 2022 1:00 p.m. - 3:00 p.m., Monday, June 13, 2022 1:24 p.m. - 1:29 p.m.

Assessing the specificity of the Rosette agent DNA amplification: An optimized protocol for the detection of standard DNA among studies.

Assessing the specificity of the Rosette agent DNA amplification: An optimized protocol for the detection of standard DNA among studies.

ScATACpipe: A nextflow pipeline for comprehensive and reproducible analyses of single cell ATAC-seq data.

Single cell ATAC-seq (scATAC-seq) has become the most widely used method for profiling open chromatin landscape of heterogeneous cell populations at a single-cell resolution. Although numerous software tools and pipelines have been developed, an easy-to-use, scalable, reproducible, and comprehensive pipeline for scATAC-seq data analyses is still lacking. To fill this gap, we developed scATACpipe, a Nextflow pipeline, for performing comprehensive analyses of scATAC-seq data including extensive quality assessment, preprocessing, dimension reduction, clustering, peak calling, differential accessibility inference, integration with scRNA-seq data, transcription factor activity and footprinting analysis, co-accessibility inference, and cell trajectory prediction. scATACpipe enables users to perform the end-to-end analysis of scATAC-seq data with three sub-workflow options for preprocessing that leverage 10x Genomics Cell Ranger ATAC software, the ultra-fast Chromap procedures, and a set of custom scripts implementing current best practices for scATAC-seq data preprocessing. The pipeline extends the R package ArchR for downstream analysis with added support to any eukaryotic species with an annotated reference genome. Importantly, scATACpipe generates an all-in-one HTML report for the entire analysis and outputs cluster-specific BAM, BED, and BigWig files for visualization in a genome browser. scATACpipe eliminates the need for users to chain different tools together and facilitates reproducible and comprehensive analyses of scATAC-seq data from raw reads to various biological insights with minimal changes of configuration settings for different computing environments or species. By applying it to public datasets, we illustrated the utility, flexibility, versatility, and reliability of our pipeline, and demonstrated that our scATACpipe outperforms other workflows.

Assessment of mitochondrial DNA viability ratio in day-4 biopsied embryos as an add-in to select euploid embryos for single embryo transfer.

The aim of this study was to assess mitochondrial DNA analysis as a predictor of the pregnancy potential of biopsied preimplantation embryos. The study included 78 blastomeres biopsied from day 4 cleavage stage euploid embryos. The embryo karyotype was confirmed by 24-chromosome preimplantation genetic testing for aneuploidies using the Illumina Next-Generation Sequencing (NGS) system. Mitochondria viability ratios (mtV) were determined from BAM files subjected to the web-based genome-analysis tool Galaxy. From this cohort of patients, 30.4% of patients (n = 34) failed to establish pregnancy. The mean mtV ratio [mean = 1.51 ± 1.25-1.77 (95% CI)] for this group was significantly (P < 0.01) lower compared with the embryo population that resulted in established pregnancies [mean = 2.5 ± 1.82-2.68 (95% CI)]. mtV multiple of mean (MoM) values were similarly significantly (P < 0.01) lower in blastocysts failing to establish pregnancy. At a 0.5 MoM cut-off, the sensitivity of mtV quantitation was 35.3% and specificity was 78.2%. The positive predictive value for an mtV value > 0.5 MoM was 41.4%. This study demonstrates the clinical utility of preimplantation quantification of viable mitochondrial DNA in biopsied blastomeres as a prognosticator of pregnancy potential.

P1261: ALLG LABORATORY SCIENCE STUDY LS21: MOLECULAR CORRELATES OF RESPONSE IN RELAPSED/REFRACTORY MARGINAL ZONE LYMPHOMA (RRMZL) PATIENTS TREATED WITH ZANUBRUTINIB IN THE MAGNOLIA TRIAL

Background: The MAGNOLIA trial demonstrated the safety and efficacy of zanubrutinib, a second generation BTK inhibitor (BTKi), in patients with rrMZL (ORR 68.2%, CR 26%, 15-month PFS 83%). An exploratory analysis of the ibrutinib-treated rrMZL study showed mutations in genes regulating NF-kB signalling pathway: TNFAIP3 (A20) and MYD88 predicted response, whereas KMT2D (MLL2) and CARD11 were associated with primary resistance. The impact of acquired mutations in BTK and PLCg2 which mediate resistance to BTKi in CLL is unknown in MZL. Aims: This Australasian Leukaemia and Lymphoma Group (ALLG) laboratory science correlative study LS21 seeks to determine if the baseline molecular profile by whole exome sequencing (WES) can predict primary resistance to zanubrutinib, and whether the emergence of resistance mutations in circulating tumour DNA (ctDNA) can herald clinical progression. Methods: DNA was extracted from baseline FFPE tumour samples from 18 patients. Longitudinal monitoring to detect the acquisition of resistance mutations was monitored in 7 patients at multiple time points using cell-free DNA (cfDNA) isolated from plasma. NGS libraries were constructed using Agilent XTHS. Unique molecular barcoding was used to facilitate error correction. Agilent WES Ver7 was used for tumour sequencing. A bespoke bait capture of 48 genes set was used for ctDNA and buccal analysis. Sequencing was performed by NovaSeq 6000 (Illumina, SP flow cell, 2x150bp chemistry) and data processing of FASTQ files via in-house bioinformatic pipeline incorporating fgbio (for UMI de-duplexing, Fulcrumgenomics), VarDict for variant calling and CNVkit for copy number analysis. Variant calls in patient samples (identified by VarDict) were manually curated from a predetermined list of 48 candidate genes by inspecting BAM files in Integrative Genomics Viewer (IGV). Mutational analysis was correlated with clinical data and survival analysis calculated using the Kaplan-Meier (log-rank) method. Results: Baseline WES identified mutations in 33/48 (69%), with a median of 5 genes affected per sample (range: 0-8). In patients treated with zanubrutinib (screen failure n=1), NFkB and NOTCH pathway genes were implicated in 13 (73%) and 10 (56%) patients respectively. KMT2D was most affected (n=7) followed by NOTCH1, NOTCH2, TNFAIP3 and MYD88 (all n=4). Patients with either MYD88 or TNFAIP3 mutations had improved median PFS (not reached (NR) vs 11.1 months, p: 0.009, HR: 0.09, 95% CI: 0.02-0.55); patients with KMT2D showed a trend to worse outcomes (PFS: 13.40 months vs NR, p: 0.06, HR 6.5, 95%CI: 1.06-38.76) [Figure 1a-c]. NOTCH mutations did not associate with outcome. CARD11 mutations were not detected. PLCy2 (H244R, L704V) mutations were detected in 2 patients prior to BTKi commencement but neither was associated with primary resistance. Within the ctDNA cohort, 8/14 (57%) mutations detected in tumour samples were present in ctDNA at study entry. Furthermore, ctDNA also harboured mutations not present in tumour WES in 4 patients, likely reflective of tumour heterogeneity. Two patients had persistent MYD88 mutations detectable in ctDNA 15 months after treatment commencement (to sensitivity of AF 0.13%). Acquired resistance mutations, BTK (C515Y/515F) and PLCy2 (R665W/R742P) were detected in 2 patients who progressed on therapy. Image:Summary/Conclusion: Mutations in genes associated with the NFkB pathway present at baseline are predictive of response to zanubrutinib in rrMZL patients. Detection of acquired BTK and PLCy2 mutations on therapy is feasible and may herald clinical disease progression.

Abstract 5694: Accurate detection of microsatellite instability (MSI) in matched and unmatched clinical tumor samples using the Pillar oncoReveal MSI panel

Abstract Introduction: MSI is caused by mutations in mismatch repair (MMR) genes that result in increased insertions and deletions within simple repeats in the genome. The MSI-High (MSI-H) phenotype is prevalent in Lynch Syndrome, caused by the inheritance of MMR loss-of-function alleles as well as in 15% of all colorectal cancers. Accurate, timely detection of MSI is important for predicting the efficacy of immunotherapies that take advantage of increased expression of neoantigens in MSI-H tumors. The Pillar oncoReveal MSI Panel is a targeted NGS assay comprised of a single-tube multiplex PCR-based chemistry and companion Pillar Variant Analysis Toolkit (PiVAT) software with MSIsensor-based detection of tumor MSI status. We report the accuracy of the PiVAT MSI module in detection of tumor MSI status without the need for a matched normal comparator. Methods: 56 pairs of clinical tumor and matched normal FFPE samples were analyzed using the oncoReveal MSI Panel. MSI status was verified by a standard MSI detection method. Illumina sequencing was performed using a PE2x150 protocol. PiVAT determines MSI status using MSIsensor. Call performance on Burrows-Wheeler Alignment (BWA) BAM files was compared with PiVAT’s paired-end assembled and filtered BAM files (PBAM). In both cases coverage normalization and FDR thresholds were set to 1. In a second experiment, 88 clinical normal tissue samples were combined with the matched tumor/normal samples and analyzed using the PiVAT MSI module’s unmatched calling protocol using a synthetic pooled normal. Results: MSI was first assessed in matched tumor/normal samples using MSIsensor. Preliminary analyses revealed a greater separation of MSI scores between MSI-positive and -negative tumors when MSIsensor was run on PBAM vs BWA-generated BAM files. Due to high individual variability at MS sites, MSI detection is difficult for tumor samples with no available matched normal tissue. Hierarchical clustering demonstrated that a subset of MS sites exhibited low variability between normal samples and showed large differences between MSI-H tumor, and MS stable (MSS) tumor and/or normal samples when compared to a pooled normal baseline. Analyses based on the full target set did not accurately detect differences between MSI-H and MSS tumor or normal samples in the pooled normal calling context. When using a clustered subset of sites, MSI-detection was comparable with the matched tumor/normal context. Conclusions: We developed a robust assay for MSI detection in multiple tumor types that encompasses library generation, data analysis, and diagnosis with 97% sensitivity and 100% specificity. We also demonstrate that accurate MSI-detection is possible in the absence of matched normal tissue, using a pooled normal reference and a curated target set. Citation Format: Jordan Aldersley, Michael Liu, Lixing Qi, Ye Jiao, Akuah Kontor, Yue Ke, Zhaohui Wang. Accurate detection of microsatellite instability (MSI) in matched and unmatched clinical tumor samples using the Pillar oncoReveal MSI panel [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5694.

Abstract 2185: MicroSEC: Sequence error filter for formalin-fixed and paraffin-embedded samples

Abstract Background: DNAs extracted from formalin-fixed and paraffin-embedded (FFPE) tissues are fragmented and contain a significant amount of single-stranded DNA (ssDNA). We found that most of the false-positive mutations in clinical sequencing with FFPE samples are ssDNA-derived artifacts. During the end-repair step of library preparation, chimeric reads are generated by mis-annealing of ssDNA molecules comprising homologous sequences with a mismatched base. The mismatched base is called as a false-positive mutation, and the position of the base is biased towards the effective ends in the individual reads because either side of the chimeric read should be soft-clipped by the mapping pipeline. Based on this theory, we developed a post hoc filter, MicroSEC, to predict such artifacts in FFPE samples. Methods: Fifty-three fresh frozen (FF) and 190 FFPE normal breast tissue samples and 23 FF and 33 FFPE breast cancer samples were obtained from 26 patients, and subjected to capture-based panel sequencing (Todai OncoPanel, TOP). We also obtained TOP data from 54 FFPE various tumor samples. MicroSEC was developed to predict artifacts with BAM files based on the positional bias of mutations within each read. The predictions were validated with amplicon-based sequencing of 97 mutations. We also developed a model which predicts artifacts only from the mutated base information without corresponding BAM files, by using 5,034 MicroSEC predictions as the supervised data with the LightGBM technique. A total of 742,030 mutations from the AACR Project GENIE were examined with the model. Results: Among the normal breast tissues, we identified 0.3 and 11.7 somatic mutations per sample in FF and FFPE specimens, and 0 (0%) and 10.1 (86.0%) were filtered out by MicroSEC, respectively. Two unique mutations with variant allele frequency of &amp;gt;50% in FFPE samples were eliminated. From the breast cancer specimens, we identified 4.0 and 10.7 mutations per sample in FF and FFPE, and 0 (0%) and 3.2 (30.2%) were filtered out, respectively. In the clinical sequencing data of 54 FFPE tumor samples, we identified 21.6 mutations per sample and 3.6 (16.6%) including five unique pathogenic mutations were filtered out. The validation study showed that the sensitivity and specificity for artifacts of MicroSEC were 97% (95% confidence interval (CI): 82%-100%) and 96% (95% CI: 88%-99%), respectively. Further, the prediction model reproduced the MicroSEC predictions with an area under the ROC curve of 0.995. The model detected 2,512 mutations (0.34%) as artifacts from Project GENIE data. There was a difference in the artifact detection rate between institutions, and 815 (1.45%) of the 56,100 mutations reported by UCSF were predicted as artifacts. Conclusions: MicroSEC removes only FFPE artifacts without eliminating true mutations found in FF samples. Our pipeline will increase the reliability of the clinical sequencing and advance cancer research using FFPE samples. Citation Format: Masachika Ikegami, Shinji Kohsaka, Takeshi Hirose, Toshihide Ueno, Satoshi Inoue, Naoki Kanomata, Hideko Yamauchi, Taisuke Mori, Shigeki Sekine, Yoshihiro Inamoto, Yasushi Yatabe, Hiroshi Kobayashi, Sakae Tanaka, Toru Akiyama, Tomotake Okuma, Hiroyuki Mano. MicroSEC: Sequence error filter for formalin-fixed and paraffin-embedded samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2185.

HapCon: estimating contamination of ancient genomes by copying from reference haplotypes.

MotivationHuman ancient DNA (aDNA) studies have surged in recent years, revolutionizing the study of the human past. Typically, aDNA is preserved poorly, making such data prone to contamination from other human DNA. Therefore, it is important to rule out substantial contamination before proceeding to downstream analysis. As most aDNA samples can only be sequenced to low coverages (<1× average depth), computational methods that can robustly estimate contamination in the low coverage regime are needed. However, the ultra low-coverage regime (0.1× and below) remains a challenging task for existing approaches.ResultsWe present a new method to estimate contamination in aDNA for male modern humans. It utilizes a Li&Stephens haplotype copying model for haploid X chromosomes, with mismatches modeled as errors or contamination. We assessed this new approach, hapCon, on simulated and down-sampled empirical aDNA data. Our experiments demonstrate that hapCon outperforms a commonly used tool for estimating male X contamination (ANGSD), with substantially lower variance and narrower confidence intervals, especially in the low coverage regime. We found that hapCon provides useful contamination estimates for coverages as low as 0.1× for SNP capture data (1240k) and 0.02× for whole genome sequencing data, substantially extending the coverage limit of previous male X chromosome-based contamination estimation methods. Our experiments demonstrate that hapCon has little bias for contamination up to 25–30% as long as the contaminating source is specified within continental genetic variation, and that its application range extends to human aDNA as old as ∼45 000 and various global ancestries.Availability and implementationWe make hapCon available as part of a python package (hapROH), which is available at the Python Package Index (https://pypi.org/project/hapROH) and can be installed via pip. The documentation provides example use cases as blueprints for custom applications (https://haproh.readthedocs.io/en/latest/hapCon.html). The program can analyze either BAM files or pileup files produced with samtools. An implementation of our software (hapCon) using Python and C is deposited at https://github.com/hyl317/hapROH.Supplementary information Supplementary data are available at Bioinformatics online.

Beginners guide to ribosome profiling

Beginners guide to ribosome profiling

EP320: AI-based method to estimate the probability of a variant being an artifact

Next Generation Sequencing is progressively becoming the leading DNA and RNA sequencing technology. It allows for massive parallel sequencing of DNA/RNA sequences in a relatively short period of time and reduced cost when compared to Sanger sequencing, producing large quantities of short sequence reads. However, NGS technologies introduce a certain amount of artifacts that are not always removed by the bioinformatics pipeline at the secondary data analysis. These artifacts can complicate the analysis of results and if they remain undetected can be a source of false positives at the variant interpretation stage. We developed an automated method based on artificial intelligence to automatically detect these artifacts so that they can be removed early in the variant interpretation pipeline avoiding false positives and saving genetic analyst precious time. A total of 200 variants were extracted from a set of 11 samples of patients coming from La Fe Hospital in Spain. These variants were a combination of real sequence variants and artifacts that were previously classified by two different human expert geneticists. A total of 80 variants were classified as real sequence variants with agreement between the two geneticists. 78 were classified as artifacts, and 42 were declared unknown by the geneticists, or a consensus about their classification was not reached. With the selected variants and artifacts a training set of 158 variants was created to feed an artificial intelligence system. For each variant, the elements used as an input of the system were : 1) sequencing depth 2) total number of reference reads 3) total number of alternative reads 4) total forward reference reads 5) total reverse reference reads 6) total forward alternative reads 7) total reverse alternative reads 8) whether the variant is a section of repetition. The system was programmed using Python programming language and NumPy and samtools libraries. In order to extract the required parameters, the system required access to both .VCF and .BAM files. After an initial training period of the system with several artificial intelligence engines, a logistic regression classifier was selected as the best performing candidate. The proposed method reported concordance with human classification in 97,7% of cases in a sample of 200 variants classified in the laboratory. Precision reached 94,11% with a recall of 100%. We presented a novel method for artefact detection in NGS tertiary analysis. The proposed methodology could be considered as a complement to existing ACMG guidelines in order to establish the validity of variant interpretation results. The method can save time and steps necessary to validate variants before being included in clinical reports. The method can be improved with the addition of new input parameters, such a nucleotides quality scores as well as cohort analysis of samples coming from the same sequencer batch.

BamToCov: an efficient toolkit for sequence coverage calculations.

MotivationMany genomics applications require the computation of nucleotide coverage of a reference genome or the ability to determine how many reads map to a reference region.ResultsBamToCov is a toolkit for rapid and flexible coverage computation that relies on the most memory efficient algorithm and is designed for integration in pipelines, given its ability to read alignment files from streams. The tools in the suite can process sorted BAM or CRAM files, allowing the user to extract coverage information via different filtering approaches and to save the output in different formats (BED, Wig or counts). The BamToCov algorithm can also handle strand-specific and/or physical coverage analyses.Availability and implementationThis program, accessory utilities and their documentation are freely available at https://github.com/telatin/BamToCov.Supplementary information Supplementary data are available at Bioinformatics online.