Efficient mapping of accurate long reads in minimizer space with mapquik

BackgroundThe human transcriptome annotation is regarded as one of the most complete of any eukaryotic species. However, limitations in sequencing technologies have biased the annotation toward multi-exonic protein coding genes. Accurate high-throughput long read transcript sequencing can now provide additional evidence for rare transcripts and genes such as mono-exonic and non-coding genes that were previously either undetectable or impossible to differentiate from sequencing noise.ResultsWe developed the Transcriptome Annotation by Modular Algorithms (TAMA) software to leverage the power of long read transcript sequencing and address the issues with current data processing pipelines. TAMA achieved high sensitivity and precision for gene and transcript model predictions in both reference guided and unguided approaches in our benchmark tests using simulated Pacific Biosciences (PacBio) and Nanopore sequencing data and real PacBio datasets. By analyzing PacBio Sequel II Iso-Seq sequencing data of the Universal Human Reference RNA (UHRR) using TAMA and other commonly used tools, we found that the convention of using alignment identity to measure error correction performance does not reflect actual gain in accuracy of predicted transcript models. In addition, inter-read error correction can cause major changes to read mapping, resulting in potentially over 6 K erroneous gene model predictions in the Iso-Seq based human genome annotation. Using TAMA’s genome assembly based error correction and gene feature evidence, we predicted 2566 putative novel non-coding genes and 1557 putative novel protein coding gene models.ConclusionsLong read transcript sequencing data has the power to identify novel genes within the highly annotated human genome. The use of parameter tuning and extensive output information of the TAMA software package allows for in depth exploration of eukaryotic transcriptomes. We have found long read data based evidence for thousands of unannotated genes within the human genome. More development in sequencing library preparation and data processing are required for differentiating sequencing noise from real genes in long read RNA sequencing data.

Human-associated metagenomic data often contain human nucleic acid information, which can affect the accuracy of microbial classification or raise ethical concerns. These reads are typically removed through alignment to the human genome using various metagenomic mapping tools or human reference genomes, followed by filtration before metagenomic analysis. In this study, we conducted a comprehensive analysis to identify the optimal combination of alignment software and human reference genomes using benchmarking data. Our findings show that the combination of bwa-mem and the telomere-to-telomere human genome (CHM13-T2T) is the most effective in removing human reads in simulated data. We also analyzed CHM13-T2T-derived sequences in RefSeq to understand how CHM13-T2T reduces false positive results. Finally, we assessed clinical samples and found that CHM13-T2T effectively reduces host-derived contamination, particularly in low microbial biomass samples. This study provides a thorough overview of the application of CHM13-T2T in metagenomic analysis and highlights its significance in improving microbial classification accuracy.IMPORTANCEHuman gene sequences account for a large proportion of metagenomic sequences. To gain accurate and precise microbiome information, effective host-derived contamination removal methods are required. Both the alignment algorithm and the reference genome could influence the effectiveness of this process. The telomere-to-telomere human genome (CHM13-T2T) is a state-of-the-art human genome with 216 Mbp of additional new sequences compared with the commonly used GRCh38.p14. Our findings show the optimal dehosting effect of CHM13-T2T combined with the bwa-mem software in metagenomic analysis. We also investigate the reasons for the superiority of CHM13-T2T. Our study provides insights into optimal strategies for host sequence removal from metagenomic data. A standard reference is proposed for future metagenomic analysis, which can improve the accuracy of microbial identification.

BackgroundVarious indexing techniques have been applied by next generation sequencing read mapping tools. The choice of a particular data structure is a trade-off between memory consumption, mapping throughput, and construction time.ResultsWe present the succinct hash index – a novel data structure for read mapping which is a variant of the classical q-gram index with a particularly small memory footprint occupying between 3.5 and 5.3 GB for a human reference genome for typical parameter settings. The succinct hash index features two novel seed selection algorithms (group seeding and variable-length seeding) and an efficient parallel construction algorithm, which we have implemented to design the FEM (Fast(F) and Efficient(E) read Mapper(M)) mapper. FEM can return all read mappings within a given edit distance. Our experimental results show that FEM is scalable and outperforms other state-of-the-art all-mappers in terms of both speed and memory footprint. Compared to Masai, FEM is an order-of-magnitude faster using a single thread and two orders-of-magnitude faster when using multiple threads. Furthermore, we observe an up to 2.8-fold speedup compared to BitMapper and an order-of-magnitude speedup compared to BitMapper2 and Hobbes3.ConclusionsThe presented succinct index is the first feasible implementation of the q-gram index functionality that occupies around 3.5 GB of memory for a whole human reference genome. FEM is freely available at https://github.com/haowenz/FEM.

Sequencing the first human genome in 2003 took 15 years and cost $2.7 billion. Advances in sequencing technologies have since decreased costs to the point where it is now feasible to resequence a whole human genome for $1000 in a single day. These advances have allowed the generation of huge volumes of high‐quality human sequence data used to construct increasingly large catalogs of both population‐level and disease‐causing variation. The existence of such databases, coupled with a high‐quality human reference genome, means we are able to interrogate and annotate all types of genetic variation and identify pathogenic variants for many diseases. Increasingly, sequencing‐based approaches are being used to elucidate the underlying genetic cause of autoimmune diseases, a group of roughly 80 polygenic diseases characterized by abnormal immune responses where healthy tissue is attacked. Although sequence data generation has become routine and affordable, significant challenges remain with no gold‐standard methodology to identify pathogenic variants currently available. This review examines the latest methodologies used to identify pathogenic variants in autoimmune diseases and considers available sequencing options and subsequent bioinformatic methodologies and strategies. The development of reliable and robust sequencing and analytic workflows to detect pathogenic variants is critical to realize the potential of precision medicine programs where patient variant information is used to inform clinical practice.

BackgroundLeveraging long-read sequencing technologies, the first complete human reference genome, T2T-CHM13, corrects assembly errors in previous references and resolves the remaining 8% of the genome. While studies on archaic admixture in modern humans have so far relied on the GRCh37 reference due to the availability of archaic genome data, the impact of T2T-CHM13 in this field remains unexplored.ResultsWe remap the sequencing reads of the high-quality Altai Neanderthal and Denisovan genomes onto GRCh38 and T2T-CHM13. Compared to GRCh37, we find that T2T-CHM13 significantly improves read mapping quality in archaic samples. We then apply IBDmix to identify Neanderthal-introgressed sequences in 2504 individuals from 26 geographically diverse populations using different reference genomes. We observe that commonly used pre-phasing filtering strategies in public datasets substantially influence archaic ancestry determination, underscoring the need for careful filter selection. Our analysis identifies approximately 51 Mb of Neanderthal sequences unique to T2T-CHM13, predominantly in genomic regions where GRCh38 and T2T-CHM13 assemblies diverge. Additionally, we uncover novel instances of population-specific archaic introgression in diverse populations, spanning genes involved in metabolism, olfaction, and ion-channel function. Finally, to facilitate the exploration of archaic alleles and adaptive signals in human genomics and evolutionary research, we integrate these introgressed sequences and adaptive signals across all reference genomes into a visualization database, ASH (www.arcseqhub.com).ConclusionsOur study enhances the detection of archaic variations in modern humans, highlights the importance of utilizing the T2T-CHM13 reference, and provides novel insights into the functional consequences of archaic hominin admixture.

<p indent="0mm">Maize (<italic>Zea mays</italic> ssp. <italic>mays</italic>, 2<italic>n</italic>=2<italic>x</italic>=20), which belongs to the <italic>Zea</italic> genera of the Maydeae tribe of the Gramineae family, is an important crop in guaranteeing national food security due to its high grain yield, forage yield and nutritive value for human food and animal feed as well as its use as an industrial material. The development and application of hybrid breeding, generous use of pesticides and fertilizers, and continuous improvement of cultivation techniques have greatly improved the yield of maize in the past several decades. However, this growth in yield has been declining. Meanwhile, the occurrence of recent extreme weather events threatens maize production. In addition, due to prevailing crop breeding practices, yield-related traits have been focused on by plant breeders, which led to narrowing genetic diversity of maize germplasm, severe cultivar homogeneity, and poor resistance to biotic and abiotic stress. Therefore, it is of the utmost importance to cultivate newer maize varieties that are highly adaptable to unfavorable environments, along with high yielding and excellent quality. Crop wild relatives (CWRs) appear promising in enhancing the genetic diversity of cultivated crops. The wild relatives of maize, such as teosinte and <italic>Tripsacum</italic> L., contain abundant genetic variation and excellent resistance to salt, cold, disease, and insect. They are a treasure for maize breeding and mining excellent exogenous gene resources. In this review, we will first discuss the phylogeny and cross-compatibility of maize and its wild relatives. Although previous studies have investigated the phylogeny and cross-compatibility of maize, teosinte and <italic>Tripsacum</italic> L. in detail, the phylogeny and cross-compatibility of maize and its wild relatives have recently been further clarified based on phenotypic, molecular geographic, and genomic evidence. Secondly, we will describe the advances in genomics of maize and its wild relatives. Over the past decade, reference genome or transcriptome sequences have been obtained for numerous crops. However, genomic studies of CWRs have trailed behind those of domesticated and other model plants. The opportunities afforded by CWRs reference genomes are only now beginning to be realized thanks to diminishing cost of next-generation sequencing technologies making population-scale data feasible. Thirdly, we will systematically cover the recent successful utilization of maize wild relatives in maize breeding for improvements in yield and quality, biotic and abiotic stress resistance, perennial maize, and forage maize. These success examples demonstrate that maize wild relatives are effective and valuable to feed back into the improvement of maize. Lastly, we will discuss the future development and research direction of maize wild relatives. It is urgent to strengthen the collection, protection and genome sequencing of maize wild relatives, and establish maize-teosinte mapping populations, including classic F₂, backcross, chromosome segment substitution line, and recombinant inbred line (RIL). Molecular markers based on the genome and transcriptome of maize and its wild relatives will be utilized to construct linkage maps and to map QTLs or genes. The cross between maize and <italic>Tripsacum</italic> is extremely difficult under the natural state, but the polyploid as bridge material (such as MTP, a maize-<italic>T</italic>.<italic> dactyloides</italic>-<italic>Z</italic>.<italic> perennis</italic> allohexaploid) can transfer <italic>T</italic>.<italic> dactyloides</italic> genes introgressed into maize. <italic>De novo</italic> domestication of maize wild relatives via gene editing technology, functional genomics and bioinformatics will accelerate maize improvement. This review aims to provide important insights into how maize wild relatives can be utilized as a powerful gene pool for improving maize varieties and guaranteeing national food security.

The human reference genome lays the basis for genomic analyses by enabling alignment of sequenced reads. However, the linear human reference genome released by the Genome Reference Consortium only represents a single consensus haplotype, and therefore constitutes a suboptimal representation of human genetic variation. Directed acyclic graph reference genome representations that incorporate information on genetic variation have been shown to improve the accuracy of read alignment, variant calling and other subsequent genomic analyses. A set of bioinformatics tools utilizing graph genomes (Seven Bridges Graph Toolkit; SBGT) for the analysis of next-generation sequencing (NGS) data has recently been published by Seven Bridges in preprint*. In this study, we benchmark the SBGT pipeline on germline variant calling and compare it against other state-of-the-art whole genome pipelines, namely BWA-MEM + GATK4 by the Broad Institute. The analysis includes five whole-genome samples (HG001-HG005) with the truth data established by the Genome in a Bottle Consortium. The results show that the SBGT pipeline has the highest accuracy in small (&lt;50bp) INDEL calling with an F1-score of 0.25% above the average, while scoring 0.06% above the average in SNP calling. Moreover, we show that, unlike the other pipelines, the SBGT pipeline is capable of finding INDELs as long as several kilobases without any additional processing steps. The Mendelian inheritance discordance measurements on CEPH/CEU and Ashkenazim trios demonstrate that the SBGT pipeline offers the most consistent germline variant calling with a score that is 1-2 percentage points better than the other pipelines. Finally, we present an optimized version of the SBGT pipeline that can process a whole-genome sample with 30x coverage on the cloud in 2 hours and under $5.

The implication of viruses in human cancers, as well as the emergence of next generation sequencing has permitted to investigate further their role and pathophysiology in the development of this disease. One such mechanism is the integration of portions of viral genomes in the human genome, as well as the specific action of viral oncogenes.inding integration sites and preserved oncogenes is still relying on heavy manual intervention. We developed an analysis and interpretation pipeline to determine viral insertions. Using data from directed viral capture, the pipeline conducts a crude genotyping phase to select reference viral genomes, identifies chimeric reads, extracts the putative human sequences to locate in the human reference genome, scores and ranks candidate junctions, and exports tabular and visual results. We leverage common bioinformatics tools (bowtie2, samtools, blat), and a dedicated filtering and ranking algorithm, implemented in R, to infer candidate junctions and insertions. Static results (tables, figures) are produced, as well as an interactive interpretation tool developed as a shiny web app. We validated this pipeline against published results of HPV, HBV, and AAV2 insertions and show good information retrieval.

Ancient DNA provides a unique frame for directly studying human population genetics in time and space. Still, since most of the ancient genomic data is low coverage, analysis is confronted with a low number of SNPs, genotype uncertainties, and reference-bias. Here, we for the first time benchmark the two distinct versions of Glimpse tools on 120 ancient human genomes from Eurasia including those largely from previously under-evaluated regions and compare the performance of genotype imputation with de facto analysis approaches for low coverage genomic data analysis. We further investigate the impact of two distinct reference panels on imputation accuracy for low coverage genomic data. We compute accuracy statistics and perform PCA and f4-statistics to explore the behaviour of genotype imputation on low coverage data regarding (i)two versions of Glimpse, (ii)two reference panels, (iii)four post-imputation filters and coverages, as well as (iv)data type and geographical origin of the samples on the analyses. Our results reveal that even for 0.1X coverage ancient human genomes, genotype imputation using Glimpse-v2 is suitable. Additionally, using the 1000 Genomes merged with Human Genome Diversity Panel improves the accuracy of imputation for the rare variants with low MAF, which might be important not only for ancient genomics but also for modern human genomic studies based on low coverage data and for haplotype-based analysis. Most importantly, we reveal that genotype imputation of low coverage ancient human genomes reduces the genetic affinity of the samples towards human reference genome. Through solving one of the most challenging biases in data analysis, so-called reference bias, genotype imputation using Glimpse v2 is promising for low coverage ancient human genomic data analysis and for rare-variant-based and haplotype-based analysis.

Mapping the Human Reference Genome’s Missing Sequence by Three-Way Admixture in Latino Genomes

I come from a family in which there have been no scientists or doctors. I was interested, however, in biology at school and started my scientific career by training in medicine at Oxford University and Guys Hospital, London. Practising as a doctor reinforced my curiosity about the biological processes underlying human disease. As a consequence, I pursued a clinical vocation in histopathology, a discipline that couples exposure to the sights and smells of the autopsy room with a daily journey into the often beautiful, sometimes ugly world of healthy and diseased human tissues under the microscope. After an introduction to general histopathology in Nick Wright's department at the Hammersmith Hospital, London, I completed my postgraduate medical training in neuropathology with Peter Lantos at the Maudsley Hospital, London.Peering at the nuclei of cancer cells under the microscope, for me it was a matter of fascination that hidden within them were the key events converting normal cells into cancer cells, and frustration because they were out of reach. Many of the tissue samples examined by pathologists are from cancers. The clonal theory of cancer development and the general role of DNA mutations in generating cancer cell clones had been established by 1986 when I was working as a junior pathologist. Indeed, the first mutated cancer gene, HRAS , had recently been identified through application of the, then new, technologies of recombinant DNA technology. Peering at the nuclei of cancer cells under the microscope, for me it was a matter of fascination that hidden within them were the key events converting normal cells into cancer cells, and frustration because they were out of reach. So, I took 3 years break from medicine to study for a PhD, learning the methods and thinking of molecular oncology in Colin Cooper's laboratory at the Institute …

Extensive Copy-Number Variation of the Human Olfactory Receptor Gene Family

Structural variation (SV) is typically defined as variation within the human genome that exceeds 50 base pairs (bp). SV may be copy number neutral or it may involve duplications, deletions, and complex rearrangements. Recent studies have shown SV to be associated with many human diseases. However, studies of SV have been challenging due to technological constraints. With the advent of third generation (long-read) sequencing technology, exploration of longer stretches of DNA not easily examined previously has been made possible. In the present study, we utilized third generation (long-read) sequencing techniques to examine SV in the EGFR landscape of four haplotypes derived from two human samples. We analyzed the EGFR gene and its landscape (+/- 500,000 base pairs) using this approach and were able to identify a region of non-coding DNA with over 90% similarity to the most common activating EGFR mutation in non-small cell lung cancer. Based on previously published Alu-element genome instability algorithms, we propose a molecular mechanism to explain how this non-coding region of DNA may be interacting with and impacting the stability of the EGFR gene and potentially generating this cancer-driver gene. By these techniques, we were also able to identify previously hidden structural variation in the four haplotypes and in the human reference genome (hg38). We applied previously published algorithms to compare the relative stabilities of these five different EGFR gene landscape haplotypes to estimate their relative potentials to generate the EGFR exon 19, 15 bp canonical deletion. To our knowledge, the present study is the first to use the differences in genomic architecture between targeted cancer-linked phased haplotypes to estimate their relative potentials to form a common cancer-linked driver mutation.

The completion of a telomere-to-telomere human reference genome, T2T-CHM13, has resolved complex regions of the genome, including repetitive and homologous regions. Here, we present a high-resolution epigenetic study of previously unresolved sequences, representing entire acrocentric chromosome short arms, gene family expansions, and a diverse collection of repeat classes. This resource precisely maps CpG methylation (32.28 million CpGs), DNA accessibility, and short-read datasets (166,058 previously unresolved chromatin immunoprecipitation sequencing peaks) to provide evidence of activity across previously unidentified or corrected genes and reveals clinically relevant paralog-specific regulation. Probing CpG methylation across human centromeres from six diverse individuals generated an estimate of variability in kinetochore localization. This analysis provides a framework with which to investigate the most elusive regions of the human genome, granting insights into epigenetic regulation.

Recent literature has found Microhomology-mediated break-induced replication (MMBIR) to be a common mechanism producing complex mutations. Complex mutations underlie the genomic instability that results in various cancers. MMBIRFinder is a tool that has successfully detected the MMBIR mutations in budding yeast DNA by comparing sampled DNA reads to a whole reference genome. A major challenge for the MMBIRFinder is the heavy computational expense needed to find MMBIR regions in human DNA, where the size of a human DNA genome is very large compared to yeast DNA. In this work, we propose a modified approach, which utilizes parallel computations to significantly reduce the processing time consumed by the MMBIFinder to search for MMBIR regions in the human genome. Moreover, the newly proposed tool preserves the validity and accuracy of the original tool for yeast, where the original MMBIRFinder often fails to operate on the much larger human genome. This will allow for identification of complex mutations generated by MMBIR in human cancer genomes, thereby improving understanding of the roles played by MMBIR in generating the mutations and rearrangements that hallmark various types of cancers and genetic disease.