Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2-5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.

/pdf/fastp-an-ultra-fast-all-in-one-fastq-preprocessor-ehzxan9vkg.pdf

fastp: an ultra-fast all-in-one FASTQ preprocessor.

Motivation: Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming, and quality filtering. These tools are often insufficiently fast as most are developed using high level programming languages (e.g., Python and Java) and provide limited multithreading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results: We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per read quality cutting, and many other operations with a single scan of the FASTQ data. It also supports unique molecular identifier preprocessing, poly tail trimming, output splitting, and base correction for paired-end data. It can automatically detect adapters for single-end and paired-end FASTQ data. This tool is developed in C++ and has multithreading support. Based on our evaluation, fastp is 2 to 5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and Implementation: The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp

https://www.biorxiv.org/content/biorxiv/early/2018/03/01/274100.full.pdf

fastp: an ultra-fast all-in-one FASTQ preprocessor

Quality control (QC) and preprocessing are essential steps for sequencing data analysis to ensure the accuracy of results. However, existing tools cannot provide a satisfying solution with integrated comprehensive functions, proper architectures, and highly scalable acceleration. In this article, we demonstrate SOAPnuke as a tool with abundant functions for a "QC-Preprocess-QC" workflow and MapReduce acceleration framework. Four modules with different preprocessing functions are designed for processing datasets from genomic, small RNA, Digital Gene Expression, and metagenomic experiments, respectively. As a workflow-like tool, SOAPnuke centralizes processing functions into 1 executable and predefines their order to avoid the necessity of reformatting different files when switching tools. Furthermore, the MapReduce framework enables large scalability to distribute all the processing works to an entire compute cluster.We conducted a benchmarking where SOAPnuke and other tools are used to preprocess a ∼30× NA12878 dataset published by GIAB. The standalone operation of SOAPnuke struck a balance between resource occupancy and performance. When accelerated on 16 working nodes with MapReduce, SOAPnuke achieved ∼5.7 times the fastest speed of other tools.

SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data.

Heightened Innate Immune Responses in the Respiratory Tract of COVID-19 Patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the global COVID-19 pandemic. Rapidly spreading SARS-CoV-2 variants may jeopardize newly introduced antibody and vaccine countermeasures. Here, using monoclonal antibodies (mAbs), animal immune sera, human convalescent sera and human sera from recipients of the BNT162b2 mRNA vaccine, we report the impact on antibody neutralization of a panel of authentic SARS-CoV-2 variants including a B.1.1.7 isolate, chimeric strains with South African or Brazilian spike genes and isogenic recombinant viral variants. Many highly neutralizing mAbs engaging the receptor-binding domain or N-terminal domain and most convalescent sera and mRNA vaccine-induced immune sera showed reduced inhibitory activity against viruses containing an E484K spike mutation. As antibodies binding to spike receptor-binding domain and N-terminal domain demonstrate diminished neutralization potency in vitro against some emerging variants, updated mAb cocktails targeting highly conserved regions, enhancement of mAb potency or adjustments to the spike sequences of vaccines may be needed to prevent loss of protection in vivo.

/pdf/resistance-of-sars-cov-2-variants-to-neutralization-by-59hsixmpwe.pdf

Resistance of SARS-CoV-2 variants to neutralization by monoclonal and serum-derived polyclonal antibodies.

Some applications, especially those clinical applications requiring high accuracy of sequencing data, usually have to face the troubles caused by unavoidable sequencing errors. Several tools have been proposed to profile the sequencing quality, but few of them can quantify or correct the sequencing errors. This unmet requirement motivated us to develop AfterQC, a tool with functions to profile sequencing errors and correct most of them, plus highly automated quality control and data filtering features. Different from most tools, AfterQC analyses the overlapping of paired sequences for pair-end sequencing data. Based on overlapping analysis, AfterQC can detect and cut adapters, and furthermore it gives a novel function to correct wrong bases in the overlapping regions. Another new feature is to detect and visualise sequencing bubbles, which can be commonly found on the flowcell lanes and may raise sequencing errors. Besides normal per cycle quality and base content plotting, AfterQC also provides features like polyX (a long sub-sequence of a same base X) filtering, automatic trimming and K-MER based strand bias profiling. For each single or pair of FastQ files, AfterQC filters out bad reads, detects and eliminates sequencer’s bubble effects, trims reads at front and tail, detects the sequencing errors and corrects part of them, and finally outputs clean data and generates HTML reports with interactive figures. AfterQC can run in batch mode with multiprocess support, it can run with a single FastQ file, a single pair of FastQ files (for pair-end sequencing), or a folder for all included FastQ files to be processed automatically. Based on overlapping analysis, AfterQC can estimate the sequencing error rate and profile the error transform distribution. The results of our error profiling tests show that the error distribution is highly platform dependent. Much more than just another new quality control (QC) tool, AfterQC is able to perform quality control, data filtering, error profiling and base correction automatically. Experimental results show that AfterQC can help to eliminate the sequencing errors for pair-end sequencing data to provide much cleaner outputs, and consequently help to reduce the false-positive variants, especially for the low-frequency somatic mutations. While providing rich configurable options, AfterQC can detect and set all the options automatically and require no argument in most cases.

/pdf/afterqc-automatic-filtering-trimming-error-removing-and-46bpjgix3w.pdf

AfterQC: automatic filtering, trimming, error removing and quality control for fastq data

Removing duplicates might be considered as a well-resolved problem in next-generation sequencing (NGS) data processing domain. However, as NGS technology gains more recognition in clinical application, researchers start to pay more attention to its sequencing errors, and prefer to remove these errors while performing deduplication operations. Recently, a new technology called unique molecular identifier (UMI) has been developed to better identify sequencing reads derived from different DNA fragments. Most existing duplicate removing tools cannot handle the UMI-integrated data. Some modern tools can work with UMIs, but are usually slow and use too much memory. Furthermore, existing tools rarely report rich statistical results, which are very important for quality control and downstream analysis. These unmet requirements drove us to develop an ultra-fast, simple, little-weighted but powerful tool for duplicate removing and sequence error suppressing, with features of handling UMIs and reporting informative results. This paper presents an efficient tool gencore for duplicate removing and sequence error suppressing of NGS data. This tool clusters the mapped sequencing reads and merges reads in each cluster to generate one single consensus read. While the consensus read is generated, the random errors introduced by library construction and sequencing can be removed. This error-suppressing feature makes gencore very suitable for the application of detecting ultra-low frequency mutations from deep sequencing data. When unique molecular identifier (UMI) technology is applied, gencore can use them to identify the reads derived from same original DNA fragment. Gencore reports statistical results in both HTML and JSON formats. The HTML format report contains many interactive figures plotting statistical coverage and duplication information. The JSON format report contains all the statistical results, and is interpretable for downstream programs. Comparing to the conventional tools like Picard and SAMtools, gencore greatly reduces the output data’s mapping mismatches, which are mostly caused by errors. Comparing to some new tools like UMI-Reducer and UMI-tools, gencore runs much faster, uses less memory, generates better consensus reads and provides simpler interfaces. To our best knowledge, gencore is the only duplicate removing tool that generates both informative HTML and JSON reports. This tool is available at: https://github.com/OpenGene/gencore

/pdf/gencore-an-efficient-tool-to-generate-consensus-reads-for-3peitv33zx.pdf

Gencore: an efficient tool to generate consensus reads for error suppressing and duplicate removing of NGS data.

Background Germline variations may contribute to lung cancer susceptibility besides environmental factors. The influence of germline mutations on lung cancer susceptibility and their correlation with somatic mutations has not been systematically investigated. Methods In this study, germline mutations from 1,026 non-small cell lung cancer (NSCLC) patients were analyzed with a 58-gene next-generation sequencing (NGS) panel containing known hereditary cancer-related genes, and were categorized based on American College of Medical Genetics and Genomics (ACMG) guidelines in pathogenicity, and the corresponding somatic mutations were analyzed using a 605-gene NGS panel containing known cancer-related genes. Results Plausible genetic susceptibility was found in 4.7% of lung cancer patients, in which 14 patients with pathogenic mutations (P group) and 34 patients with likely-pathogenic mutations (LP group) were identified. The ratio of the first degree relatives with lung cancer history of the P groups was significantly higher than the Non-P group (P=0.009). The ratio of lung cancer patients with history of other cancers was higher in P (P=0.0007) or LP (P=0.017) group than the Non-P group. Pathogenic mutations fell most commonly in BRCA2, followed by CHEK2 and ATM. Likely-pathogenic mutations fell most commonly in NTRK1 and EXT2, followed by BRIP1 and PALB2. These genes are involved in DNA repair, cell cycle regulation and tumor suppression. By comparing the germline mutation frequency from this study with that from the whole population or East Asian population (gnomAD database), we found that the overall odds ratio (OR) for P or LP group was 17.93 and 15.86, respectively, when compared with the whole population, and was 2.88 and 3.80, respectively, when compared with the East Asian population, suggesting the germline mutations of the P and LP groups were risk factors for lung cancer. Somatic mutation analysis revealed no significant difference in tumor mutation burden (TMB) among the groups, although a trend of lower TMB in the pathogenic group was found. The SNV/INDEL mutation frequency of TP53 in the P group was significantly lower than the other two groups, and the copy number variation (CNV) mutation frequency of PIK3CA and MET was significantly higher than the Non-P group. Pathway enrichment analysis found no significant difference in aberrant pathways among the three groups. Conclusions A proportion of 4.7% of patients carrying germline variants may be potentially linked to increased susceptibility to lung cancer. Patients with pathogenic germline mutations exhibited stronger family history and higher lung cancer risk.

/pdf/the-contribution-of-hereditary-cancer-related-germline-acbwslxrrz.pdf

Yanqing Zhou

Papers

fastp: an ultra-fast all-in-one FASTQ preprocessor.

fastp: an ultra-fast all-in-one FASTQ preprocessor

AfterQC: automatic filtering, trimming, error removing and quality control for fastq data

Gencore: an efficient tool to generate consensus reads for error suppressing and duplicate removing of NGS data.

The contribution of hereditary cancer-related germline mutations to lung cancer susceptibility