Accurate Reads Research Articles

Fast and accurate short-read alignment with hybrid hash-tree data structure

Rapidly increasing the amount of short-read data generated by NGSs (new-generation sequencers) calls for the development of fast and accurate read alignment programs. The programs based on the hash table (BLAST) and Burrows-Wheeler transform (bwa-mem) are used, and the latter is known to give superior performance. We here present a new algorithm, a hybrid of hash table and suffix tree, which we designed to speed up the alignment of short reads against large reference sequences such as the human genome. The total turnaround time for processing one human genome sample (read depth of 30) is just 31 min with our system while that was more than 25 h with bwa-mem/gatk. The time for the aligner alone is 28 min for our system but around 2 h for bwa-mem. Our new algorithm is 4.4 times faster than bwa-mem while achieving similar accuracy. Variant calling and other downstream analyses after the alignment can be done with open-source tools such as SAMtools and Genome Analysis Toolkit (gatk) packages, as well as our own fast variant caller, which is well parallelized and much faster than gatk.

Microsatellites explorer: A database of short tandem repeats across genomes

Short tandem repeats (STRs) are widespread, repetitive elements, with a number of biological functions and are among the most rapidly mutating regions in the genome. Their distribution varies significantly between taxonomic groups in the tree of life and are highly polymorphic within the human population. Advances in sequencing technologies coupled with decreasing costs have enabled the generation of an ever-growing number of complete genomes. Additionally, the arrival of accurate long reads has facilitated the generation of Telomere-to-Telomere (T2T) assemblies of complete genomes. Nevertheless, there is no comprehensive database that encompasses the STRs found per genome across different organisms and for different human genomes across diverse ancestries. Here we introduce Microsatellites Explorer, a database of STRs found in the genomes of 117,253 organisms across all major taxonomic groups, 15 T2T genome assemblies of different organisms, and 94 human haplotypes from the human pangenome. The database currently hosts 406,758,798 STR sequences, serving as a centralized user-friendly repository to perform searches, interactive visualizations, and download existing STR data for independent analysis. Microsatellites Explorer is implemented as a web-portal for browsing, analyzing and downloading STR data. Microsatellites Explorer is publicly available at https://www.microsatellitesexplorer.com.

Assembly-free reads accurate identification (AFRAID) approach outperforms other methods of DNA barcoding in the walnut family (Juglandaceae)

DNA barcoding has been extensively used for species identification. However, species identification of mixed samples or degraded DNA is limited by current DNA barcoding methods. In this study, we use plant species in Juglandaceae to evaluate an assembly-free accurate reads identification (AFRAID) method of species identification, a novel approach for precise species identification in plants. Specifically, we determined (1) the accuracy of DNA barcoding approaches in delimiting species in Juglandaceae, (2) the minimum size of chloroplast dataset for species discrimination, and (3) minimum amount of next generation sequencing (NGS) data required for species identification. We found that species identification rates were highest when whole chloroplast genomes were used, followed by taxon-specific DNA barcodes, and then universal DNA barcodes. Species identification of 100% was achieved when chloroplast genome sequence coverage reached 20% and the original sequencing data reached 500,000 reads. AFRAID accurately identified species for all samples tested after 500,000 clean reads, with far less computing time than common approaches. These results provide a new approach to accurately identify species, overcoming limitations of traditional DNA barcodes. Our method, which uses next generation sequencing to generate partial chloroplast genomes, reveals that DNA barcode regions are not necessarily fixed, accelerating the process of species identification.

Enhancing transcriptome expression quantification through accurate assignment of long RNA sequencing reads with TranSigner.

Recently developed long-read RNA sequencing technologies promise to provide a more accurate and comprehensive view of transcriptomes compared to short-read sequencers, primarily due to their capability to achieve full-length sequencing of transcripts. However, realizing this potential requires computational tools tailored to process long reads, which exhibit a higher error rate than short reads. Existing methods for assembling and quantifying long-read data often disagree on expressed transcripts and their abundance levels, leading researchers to lack confidence in the transcriptomes produced using this data. One approach to address the uncertainties in transcriptome assembly and quantification is by assigning the long reads to transcripts, enabling a more detailed characterization of transcript support at the read level. Here, we introduce TranSigner, a versatile tool that assigns long reads to any input transcriptome. TranSigner consists of three consecutive modules performing: read alignment to the given transcripts, computation of read-to-transcript compatibility based on alignment scores and positions, and execution of an expectation-maximization algorithm to probabilistically assign reads to transcripts and estimate transcript abundances. Using simulated data and experimental datasets from three well-studied organisms - Homo sapiens, Arabidopsis thaliana, and Mus musculus - we demonstrate that TranSigner achieves accurate read assignments, obtaining higher accuracy in transcript abundance estimation compared to existing tools.

Maptcha: an efficient parallel workflow for hybrid genome scaffolding

BackgroundGenome assembly, which involves reconstructing a target genome, relies on scaffolding methods to organize and link partially assembled fragments. The rapid evolution of long read sequencing technologies toward more accurate long reads, coupled with the continued use of short read technologies, has created a unique need for hybrid assembly workflows. The construction of accurate genomic scaffolds in hybrid workflows is complicated due to scale, sequencing technology diversity (e.g., short vs. long reads, contigs or partial assemblies), and repetitive regions within a target genome.ResultsIn this paper, we present a new parallel workflow for hybrid genome scaffolding that would allow combining pre-constructed partial assemblies with newly sequenced long reads toward an improved assembly. More specifically, the workflow, called Maptcha, is aimed at generating long scaffolds of a target genome, from two sets of input sequences—an already constructed partial assembly of contigs, and a set of newly sequenced long reads. Our scaffolding approach internally uses an alignment-free mapping step to build a ⟨\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\langle $$\\end{document}contig,contig⟩\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rangle $$\\end{document} graph using long reads as linking information. Subsequently, this graph is used to generate scaffolds. We present and evaluate a graph-theoretic “wiring” heuristic to perform this scaffolding step. To enable efficient workload management in a parallel setting, we use a batching technique that partitions the scaffolding tasks so that the more expensive alignment-based assembly step at the end can be efficiently parallelized. This step also allows the use of any standalone assembler for generating the final scaffolds.ConclusionsOur experiments with Maptcha on a variety of input genomes, and comparison against two state-of-the-art hybrid scaffolders demonstrate that Maptcha is able to generate longer and more accurate scaffolds substantially faster. In almost all cases, the scaffolds produced by Maptcha are at least an order of magnitude longer (in some cases two orders) than the scaffolds produced by state-of-the-art tools. Maptcha runs significantly faster too, reducing time-to-solution from hours to minutes for most input cases. We also performed a coverage experiment by varying the sequencing coverage depth for long reads, which demonstrated the potential of Maptcha to generate significantly longer scaffolds in low coverage settings (1×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1\ imes $$\\end{document}–10×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10\ imes $$\\end{document}).

Benchmarking short-, long- and hybrid-read assemblers for metagenome sequencing of complex microbial communities.

Metagenome community analyses, driven by the continued development in sequencing technology, is rapidly providing insights in many aspects of microbiology and becoming a cornerstone tool. Illumina, Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) are the leading technologies, each with their own advantages and drawbacks. Illumina provides accurate reads at a low cost, but their length is too short to close bacterial genomes. Long reads overcome this limitation, but these technologies produce reads with lower accuracy (ONT) or with lower throughput (PacBio high-fidelity reads). In a critical first analysis step, reads are assembled to reconstruct genomes or individual genes within the community. However, to date, the performance of existing assemblers has never been challenged with a complex mock metagenome. Here, we evaluate the performance of current assemblers that use short, long or both read types on a complex mock metagenome consisting of 227 bacterial strains with varying degrees of relatedness. We show that many of the current assemblers are not suited to handle such a complex metagenome. In addition, hybrid assemblies do not fulfil their potential. We conclude that ONT reads assembled with CANU and Illumina reads assembled with SPAdes offer the best value for reconstructing genomes and individual genes of complex metagenomes, respectively.

Mora: abundance aware metagenomic read re-assignment for disentangling similar strains

BackgroundTaxonomic classification of reads obtained by metagenomic sequencing is often a first step for understanding a microbial community, but correctly assigning sequencing reads to the strain or sub-species level has remained a challenging computational problem.ResultsWe introduce Mora, a MetagenOmic read Re-Assignment algorithm capable of assigning short and long metagenomic reads with high precision, even at the strain level. Mora is able to accurately re-assign reads by first estimating abundances through an expectation-maximization algorithm and then utilizing abundance information to re-assign query reads. The key idea behind Mora is to maximize read re-assignment qualities while simultaneously minimizing the difference from estimated abundance levels, allowing Mora to avoid over assigning reads to the same genomes. On simulated diverse reads, this allows Mora to achieve F1 scores comparable to other algorithms while having less runtime. However, Mora significantly outshines other algorithms on very similar reads. We show that the high penalty of over assigning reads to a common reference genome allows Mora to accurately infer correct strains for real data in the form of E. coli reads.ConclusionsMora is a fast and accurate read re-assignment algorithm that is modularized, allowing it to be incorporated into general metagenomics and genomics workflows. It is freely available at https://github.com/AfZheng126/MORA.

Space-efficient computation of k-mer dictionaries for large values of k

Computing k-mer frequencies in a collection of reads is a common procedure in many genomic applications. Several state-of-the-art k-mer counters rely on hash tables to carry out this task but they are often optimised for small k as a hash table keeping keys explicitly (i.e., k-mer sequences) takes O(Nkw)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(N\\frac{k}{w})$$\\end{document} computer words, N being the number of distinct k-mers and w the computer word size, which is impractical for long values of k. This space usage is an important limitation as analysis of long and accurate HiFi sequencing reads can require larger values of k. We propose Kaarme, a space-efficient hash table for k-mers using O(N+ukw)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(N+u\\frac{k}{w})$$\\end{document} words of space, where u is the number of reads. Our framework exploits the fact that consecutive k-mers overlap by k-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k-1$$\\end{document} symbols. Thus, we only store the last symbol of a k-mer and a pointer within the hash table to a previous one, which we can use to recover the remaining k-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k-1$$\\end{document} symbols. We adapt Kaarme to compute canonical k-mers as well. This variant also uses pointers within the hash table to save space but requires more work to decode the k-mers. Specifically, it takes O(σk)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(\\sigma ^{k})$$\\end{document} time in the worst case, σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma$$\\end{document} being the DNA alphabet, but our experiments show this is hardly ever the case. The canonical variant does not improve our theoretical results but greatly reduces space usage in practice while keeping a competitive performance to get the k-mers and their frequencies. We compare canonical Kaarme to a regular hash table storing canonical k-mers explicitly as keys and show that our method uses up to five times less space while being less than 1.5 times slower. We also show that canonical Kaarme uses significantly less memory than state-of-the-art k-mer counters when they do not resort to disk to keep intermediate results.

A machine-readable specification for genomics assays.

Understanding the structure of sequenced fragments from genomics libraries is essential for accurate read preprocessing. Currently, different assays and sequencing technologies require custom scripts and programs that do not leverage the common structure of sequence elements present in genomics libraries. We present seqspec, a machine-readable specification for libraries produced by genomics assays that facilitates standardization of preprocessing and enables tracking and comparison of genomics assays. The specification and associated seqspec command line tool is available at https://www.doi.org/10.5281/zenodo.10213865.

Abstract 2934: Somatic variant workflow with HiFi sequencing provides new insights in highly challenging cancer cases

Abstract Tumor evolution is a highly heterogeneous process where multiple oncogenic pathways can lead to host defense evasion. Advancements in genome sequencing are allowing us to better understand these processes and their impact. However, critical genomic variations that could be pivotal to the development of tumors may have remained undetected due to technological constraints of short-read sequencing. PacBio HiFi sequencing generates accurate (>99.9%) long reads (>15 kb) with native 5-methylcytosine information that can comprehensively delineate many variations, including ones which were previously inaccessible with short-read sequencing. Unlike standard whole-genome analysis, cancer variant calling must account for somatic variation, where the assumptions of a diploid genome are violated. We have developed and optimized a bioinformatics workflow for somatic variant detection with HiFi sequence data. To validate this pipeline, we sequenced two well-studied cancer cell lines (COLO829, HCC1395) to 60-fold depth and their matched normal lymphoblast cells (40-fold to 60-fold depth) and called all classes of variations (SNVs, INDELS, and structural variants). Next, we compared our calls against the Valle-Inclan (structural variants) and SEQC2 (small variants: SNV/INDEL) datasets. We achieved 92% F1 score for small variants across all variant allele frequencies (VAF) in the HCC1395 experiment. We observed a maintained accuracy (F1=91%) even at lower variant frequencies (10-20), highlighting the ability of HiFi sequencing to detect subclonal mutations. The workflow calls structural variants with high sensitivity, capturing 57/62 (92%) of all previously validated SVs in COLO829 at all VAF. Lastly, we applied our pipeline to five diverse tumor samples that exhibit complex oncogenic phenotypes including homologous recombination deficiency (HRD) and high genomic instability, e.g. unresolved rearrangement breakpoints in repetitive regions and unknown oncogenic drivers when prior sequencing efforts did not provide full clarity as to the molecular drivers of tumorigenesis. Notably, we were able to identify and phase 5mC methylation of promoters together with large-scale structural variants in cancer genes involved in HRD. Taken together, we demonstrated the high accuracy of HiFi reads in resolving somatic variants and its potential to provide novel insights in complex cancer samples. The ability for long reads to resolve complex genomics aberrations and epigenetic markers that underly the tumorigenesis process will become indispensable in the realm of precision oncology. Citation Format: Khi Pin Chua, Ian McLaughlin, Oliver Hofmann, Kym Pham Stewart, Primo Baybayan, Jonathan Bibliowicz, Sean Grimmond, Zev Kronenberg, Michael A. Eberle. Somatic variant workflow with HiFi sequencing provides new insights in highly challenging cancer cases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2934.

Ribotin: automated assembly and phasing of rDNA morphs.

The ribosomal DNA (rDNA) arrays are highly repetitive and homogenous regions which exist in all life. Due to their repetitiveness, current assembly methods do not fully assemble the rDNA arrays in humans and many other eukaryotes, and so variation within the rDNA arrays cannot be effectively studied. Here, we present the tool ribotin to assemble full length rDNA copies, or morphs. Ribotin uses a combination of highly accurate long reads and extremely long nanopore reads to resolve the variation between rDNA morphs. We show that ribotin successfully recovers the most abundant morphs in human and nonhuman genomes. We also find that genome wide consensus sequences of the rDNA arrays frequently produce a mosaic sequence that does not exist in the genome. Ribotin is available on https://github.com/maickrau/ribotin and as a package on bioconda.

A pooled-sample draft genome assembly provides insights into host plant-specific transcriptional responses of a Solanaceae-specializing pest, Tupiocoris notatus (Hemiptera: Miridae).

The assembly of genomes from pooled samples of genetically heterogenous samples of conspecifics remains challenging. In this study, we show that high-quality genome assemblies can be produced from samples of multiple wild-caught individuals. We sequenced DNA extracted from a pooled sample of conspecific herbivorous insects (Hemiptera: Miridae: Tupiocoris notatus) acquired from a greenhouse infestation in Tucson, Arizona (in the range of 30-100 individuals; 0.5 mL tissue by volume) using PacBio highly accurate long reads (HiFi). The initial assembly contained multiple haplotigs (>85% BUSCOs duplicated), but duplicate contigs could be easily purged to reveal a highly complete assembly (95.6% BUSCO, 4.4% duplicated) that is highly contiguous by short-read assembly standards (N 50 = 675 kb; Largest contig = 4.3 Mb). We then used our assembly as the basis for a genome-guided differential expression study of host plant-specific transcriptional responses. We found thousands of genes (N = 4982) to be differentially expressed between our new data from individuals feeding on Datura wrightii (Solanaceae) and existing RNA-seq data from Nicotiana attenuata (Solanaceae)-fed individuals. We identified many of these genes as previously documented detoxification genes such as glutathione-S-transferases, cytochrome P450s, and UDP-glucosyltransferases. Together our results show that long-read sequencing of pooled samples can provide a cost-effective genome assembly option for small insects and can provide insights into the genetic mechanisms underlying interactions between plants and herbivorous pests.

MAC-ErrorReads: machine learning-assisted classifier for filtering erroneous NGS reads.

The rapid advancement of next-generation sequencing (NGS) machines in terms of speed and affordability has led to the generation of a massive amount of biological data at the expense of data quality as errors become more prevalent. This introduces the need to utilize different approaches to detect and filtrate errors, and data quality assurance is moved from the hardware space to the software preprocessing stages. We introduce MAC-ErrorReads, a novel Machine learning-Assisted Classifier designed for filtering Erroneous NGS Reads. MAC-ErrorReads transforms the erroneous NGS read filtration process into a robust binary classification task, employing five supervised machine learning algorithms. These models are trained on features extracted through the computation of Term Frequency-Inverse Document Frequency (TF_IDF) values from various datasets such as E. coli, GAGE S. aureus, H. Chr14, Arabidopsis thaliana Chr1 and Metriaclima zebra. Notably, Naive Bayes demonstrated robust performance across various datasets, displaying high accuracy, precision, recall, F1-score, MCC, and ROC values. The MAC-ErrorReads NB model accurately classified S. aureus reads, surpassing most error correction tools with a 38.69% alignment rate. For H. Chr14, tools like Lighter, Karect, CARE, Pollux, and MAC-ErrorReads showed rates above 99%. BFC and RECKONER exceeded 98%, while Fiona had 95.78%. For the Arabidopsis thaliana Chr1, Pollux, Karect, RECKONER, and MAC-ErrorReads demonstrated good alignment rates of 92.62%, 91.80%, 91.78%, and 90.87%, respectively. For the Metriaclima zebra, Pollux achieved a high alignment rate of 91.23%, despite having the lowest number of mapped reads. MAC-ErrorReads, Karect, and RECKONER demonstrated good alignment rates of 83.76%, 83.71%, and 83.67%, respectively, while also producing reasonable numbers of mapped reads to the reference genome. This study demonstrates that machine learning approaches for filtering NGS reads effectively identify and retain the most accurate reads, significantly enhancing assembly quality and genomic coverage. The integration of genomics and artificial intelligence through machine learning algorithms holds promise for enhancing NGS data quality, advancing downstream data analysis accuracy, and opening new opportunities in genetics, genomics, and personalized medicine research.

High-quality metagenome assembly from long accurate reads with metaMDBG

We introduce metaMDBG, a metagenomics assembler for PacBio HiFi reads. MetaMDBG combines a de Bruijn graph assembly in a minimizer space with an iterative assembly over sequences of minimizers to address variations in genome coverage depth and an abundance-based filtering strategy to simplify strain complexity. For complex communities, we obtained up to twice as many high-quality circularized prokaryotic metagenome-assembled genomes as existing methods and had better recovery of viruses and plasmids.

A Multi-bit ECRAM-Based Analog Neuromorphic System with High-Precision Current Readout Achieving 97.3% Inference Accuracy.

This article proposes an analog neuromorphic system that enhances symmetry, linearity, and endurance by using a high-precision current readout circuit for multi-bit nonvolatile electro-chemical random-access memory (ECRAM). For on-chip training and inference, the system uses activation modules and matrix processing units to manage analog update/read paths and perform precise output sensing with feedback-based current scaling on the ECRAM array. The 250nm CMOS neuromorphic chip was tested with a 32 x 32 ECRAM synaptic array, achieving linear and symmetric updates and accurate read operations. The proposed circuit system updates the 32 x 32 ECRAM across 100 levels, maintaining consistent synaptic weights, and operates with an output error rate of up to 2.59% per column. It consumes 5.9 mW of power excluding the ECRAM array and achieves 97.3% inference accuracy on the MNIST dataset, close to the software-confirmed 97.78%, with only the final layer (64 x 10) mapped to the ECRAM.

De novo reconstruction of satellite repeat units from sequence data.

Satellite DNA are long tandemly repeating sequences in a genome and may be organized as high-order repeats (HORs). They are enriched in centromeres and are challenging to assemble. Existing algorithms for identifying satellite repeats either require the complete assembly of satellites or only work for simple repeat structures without HORs. Here we describe Satellite Repeat Finder (SRF), a new algorithm for reconstructing satellite repeat units and HORs from accurate reads or assemblies without prior knowledge on repeat structures. Applying SRF to real sequence data, we show that SRF could reconstruct known satellites in human and well-studied model organisms. We also find satellite repeats are pervasive in various other species, accounting for up to 12% of their genome contents but are often underrepresented in assemblies. With the rapid progress in genome sequencing, SRF will help the annotation of new genomes and the study of satellite DNA evolution even if such repeats are not fully assembled.

Pathologic Processing of Lung Cancer Resection Specimens After Neoadjuvant Therapy

Neoadjuvant treatment of non-small cell lung cancer challenges the traditional processing of pathology specimens. Induction therapy before resection allows evaluation of the efficacy of neoadjuvant agents at the time of surgery. Many clinical trials use pathologic tumor response, measured as major pathologic response (MPR, ≤10% residual viable tumor [RVT]) or complete pathologic response (CPR, 0% RVT) as a surrogate of clinical efficacy. Consequently, accurate pathologic evaluation of RVT is crucial. However, pathologic assessment has not been uniform, which is particularly true for sampling of the primary tumor, which instead of the traditional processing, requires different tissue submission because the focus has shifted from tumor typing alone to RVT scoring. Using a simulation study, we analyzed the accuracy rates of %RVT, MPR, and CPR of 31 pretreated primary lung tumors using traditional grossing compared with the gold standard of submitting the entire residual primary tumor and identified the minimum number of tumor sections to be submitted to ensure the most accurate scoring of %RVT, MPR, and CPR. Accurate %RVT, MPR, and CPR calls were achieved in 52%, 87%, and 81% of cases, respectively, using the traditional grossing method. Accuracy rates of at least 90% for these parameters require either submission of all residual primary tumor or at least 20 tumor sections. Accurate %RVT, MPR, and CPR scores cannot be achieved with traditional tumor grossing. Submission of the entire primary tumor, up to a maximum of 20 sections, is required for the most accurate reads.

TAGET: a toolkit for analyzing full-length transcripts from long-read sequencing

Single-molecule Real-time Isoform Sequencing (Iso-seq) of transcriptomes by PacBio can generate very long and accurate reads, thus providing an ideal platform for full-length transcriptome analysis. We present an integrated computational toolkit named TAGET for Iso-seq full-length transcript data analyses, including transcript alignment, annotation, gene fusion detection, and quantification analyses such as differential expression gene analysis and differential isoform usage analysis. We evaluate the performance of TAGET using a public Iso-seq dataset and newly sequenced Iso-seq datasets from tumor patients. TAGET gives significantly more precise novel splice site prediction and enables more accurate novel isoform and gene fusion discoveries, as validated by experimental validations and comparisons with RNA-seq data. We identify and experimentally validate a differential isoform usage gene ECM1, and further show that its isoform ECM1b may be a tumor-suppressor in laryngocarcinoma. Our results demonstrate that TAGET provides a valuable computational toolkit and can be applied to many full-length transcriptome studies.

A landscape of complex tandem repeats within individual human genomes

Markedly expanded tandem repeats (TRs) have been correlated with ~60 diseases. TR diversity has been considered a clue toward understanding missing heritability. However, haplotype-resolved long TRs remain mostly hidden or blacked out because their complex structures (TRs composed of various units and minisatellites containing >10-bp units) make them difficult to determine accurately with existing methods. Here, using a high-precision algorithm to determine complex TR structures from long, accurate reads of PacBio HiFi, an investigation of 270 Japanese control samples yields several genome-wide findings. Approximately 322,000 TRs are difficult to impute from the surrounding single-nucleotide variants. Greater genetic divergence of TR loci is significantly correlated with more events of younger replication slippage. Complex TRs are more abundant than single-unit TRs, and a tendency for complex TRs to consist of <10-bp units and single-unit TRs to be minisatellites is statistically significant at loci with ≥500-bp TRs. Of note, 8909 loci with extended TRs (>100b longer than the mode) contain several known disease-associated TRs and are considered candidates for association with disorders. Overall, complex TRs and minisatellites are found to be abundant and diverse, even in genetically small Japanese populations, yielding insights into the landscape of long TRs.

Identifying and quantifying isoforms from accurate full-length transcriptome sequencing reads with Mandalorion

In this manuscript, we introduce and benchmark Mandalorion v4.1 for the identification and quantification of full-length transcriptome sequencing reads. It further improves upon the already strong performance of Mandalorion v3.6 used in the LRGASP consortium challenge. By processing real and simulated data, we show three main features of Mandalorion: first, Mandalorion-based isoform identification has very high precision and maintains high recall even in the absence of any genome annotation. Second, isoform read counts as quantified by Mandalorion show a high correlation with simulated read counts. Third, isoforms identified by Mandalorion closely reflect the full-length transcriptome sequencing data sets they are based on.