Bioinformatics advances最新文献

{"title":"scVIC: Deep generative modeling of heterogeneity for scRNA-seq data","authors":"Jiankang Xiong, Fuzhou Gong, Liang Ma, Lin Wan","doi":"10.1093/bioadv/vbae086","DOIUrl":"https://doi.org/10.1093/bioadv/vbae086","url":null,"abstract":"\u0000 \u0000 \u0000 Single-cell RNA sequencing (scRNA-seq) has become a valuable tool for studying cellular heterogeneity. However, the analysis of scRNA-seq data is challenging because of inherent noise and technical variability. Existing methods often struggle to simultaneously explore heterogeneity across cells, handle dropout events, and account for batch effects. These drawbacks call for a robust and comprehensive method that can address these challenges and provide accurate insights into heterogeneity at the single-cell level.\u0000 \u0000 \u0000 \u0000 In this study, we introduce scVIC, an algorithm designed to account for variational inference, while simultaneously handling biological heterogeneity and batch effects at the single-cell level. scVIC explicitly models both biological heterogeneity and technical variability to learn cellular heterogeneity in a manner free from dropout events and the bias of batch effects. By leveraging variational inference, we provide a robust framework for inferring the parameters of scVIC. To test the performance of scVIC, we employed both simulated and biological scRNA-seq datasets, either including, or not, batch effects. scVIC was found to outperform other approaches because of its superior clustering ability and circumvention of the batch effects problem.\u0000 \u0000 \u0000 \u0000 The code of scVIC and replication for this study are available at https://github.com/HiBearME/scVIC/tree/v1.0.\u0000 \u0000 \u0000 \u0000 Supplementary data are available at Bioinformatics Advances online.\u0000","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141349305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Demultiplexing of Single-Cell RNA sequencing data using interindividual variation in gene expression","authors":"I. Nassiri, Andrew J Kwok, Aneesha Bhandari, Katherine R. Bull, Lucy C. Garner, Paul Klenerman, Caleb Webber, Laura Parkkinen, Angela W Lee, Yanxia Wu, Benjamin Fairfax, Julian C. Knight, David Buck, Paolo Piazza","doi":"10.1093/bioadv/vbae085","DOIUrl":"https://doi.org/10.1093/bioadv/vbae085","url":null,"abstract":"\u0000 \u0000 \u0000 Pooled designs for single-cell RNA sequencing, where many cells from distinct samples are processed jointly, offer increased throughput and reduced batch variation. This study describes expression-aware demultiplexing (EAD), a computational method that employs differential co-expression patterns between individuals to demultiplex pooled samples without any extra experimental steps.\u0000 \u0000 \u0000 \u0000 We use synthetic sample pools and show that the top interindividual differentially co-expressed genes provide a distinct cluster of cells per individual, significantly enriching the regulation of metabolism. Our application of EAD to samples of 6 isogenic inbred mice demonstrated that controlling genetic and environmental effects can solve inter-individual variations related to metabolic pathways. We utilized 30 samples from both sepsis and healthy individuals in six batches to assess the performance of classification approaches. The results indicate that combining genetic and EAD results can enhance the accuracy of assignments (Min 0.94, Mean 0.98, Max 1). The results were enhanced by an average of 1.4% when EAD and barcoding techniques were combined (Min. 1.25%, Median 1.33%, Max. 1.74%). Furthermore, we demonstrate that interindividual differential co-expression analysis within the same cell type can be used to identify cells from the same donor in different activation states. By analyzing single-nuclei transcriptome profiles from the brain, we demonstrate that our method can be applied to non-immune cells.\u0000 \u0000 \u0000 \u0000 Expression-aware demultiplexing workflow is available at https://isarnassiri.github.io/scDIV/ as an R package called scDIV (acronym for Single Cell RNA sequencing data Demultiplexing using Interindividual Variations).\u0000 \u0000 \u0000 \u0000 Supplementary data are available at Bioinformatics Advances online.\u0000","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141370591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"OM2Seq: learning retrieval embeddings for optical genome mapping.","authors":"Yevgeni Nogin, Danielle Sapir, Tahir Detinis Zur, Nir Weinberger, Yonatan Belinkov, Yuval Ebenstein, Yoav Shechtman","doi":"10.1093/bioadv/vbae079","DOIUrl":"10.1093/bioadv/vbae079","url":null,"abstract":"<p><strong>Motivation: </strong>Genomics-based diagnostic methods that are quick, precise, and economical are essential for the advancement of precision medicine, with applications spanning the diagnosis of infectious diseases, cancer, and rare diseases. One technology that holds potential in this field is optical genome mapping (OGM), which is capable of detecting structural variations, epigenomic profiling, and microbial species identification. It is based on imaging of linearized DNA molecules that are stained with fluorescent labels, that are then aligned to a reference genome. However, the computational methods currently available for OGM fall short in terms of accuracy and computational speed.</p><p><strong>Results: </strong>This work introduces OM2Seq, a new approach for the rapid and accurate mapping of DNA fragment images to a reference genome. Based on a Transformer-encoder architecture, OM2Seq is trained on acquired OGM data to efficiently encode DNA fragment images and reference genome segments to a common embedding space, which can be indexed and efficiently queried using a vector database. We show that OM2Seq significantly outperforms the baseline methods in both computational speed (by 2 orders of magnitude) and accuracy.</p><p><strong>Availability and implementation: </strong>https://github.com/yevgenin/om2seq.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11194751/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hapsolutely: a user-friendly tool integrating haplotype phasing, network construction and haploweb calculation","authors":"Miguel Vences, Stefanos Patmanidis, Jan-Christopher Schmidt, Michael Matschiner, Aurélien Miralles, Susanne S Renner","doi":"10.1093/bioadv/vbae083","DOIUrl":"https://doi.org/10.1093/bioadv/vbae083","url":null,"abstract":"\u0000 \u0000 \u0000 Haplotype networks are a routine approach to visualize relationships among alleles. Such visual analysis of single-locus data is still of importance, especially in species diagnosis and delimitation, where a limited amount of sequence data usually are available and sufficient, along with other data sets in the framework of integrative taxonomy. In diploid organisms, this often requires separating (‘phasing’) sequences with heterozygotic positions, and typically separate programs are required for phasing, reformatting of input files, and haplotype network construction. We therefore developed Hapsolutely, a user-friendly program with an ergonomic graphical user interface (GUI) that integrates haplotype phasing from single-locus sequences with five approaches for network/genealogy reconstruction.\u0000 \u0000 \u0000 \u0000 Among the novel options implemented, Hapsolutely integrates phasing and graphical reconstruction steps of haplotype networks, supports input of species partition data in the common SPART and SPART-XML formats, and calculates and visualizes haplowebs and fields for recombination, thus allowing graphical comparison of allele distribution and allele sharing among subsets for the purpose of species delimitation. The new tool has been specifically developed with a focus on the workflow in alpha-taxonomy, where exploring fields for recombination across alternative species partitions may help species delimitation.\u0000 \u0000 \u0000 \u0000 Hapsolutely is written in Python, and integrates code from Phase, SeqPHASE and PopART in C ++ and Haxe. Compiled stand-alone executables for MS Windows and Mac OS along with a detailed manual can be downloaded from https://www.itaxotools.org; the source code is openly available on GitHub (https://github.com/iTaxoTools/Hapsolutely).\u0000","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141382901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CellsFromSpace: a fast, accurate, and reference-free tool to deconvolve and annotate spatially distributed omics data.","authors":"Corentin Thuilliez, Gaël Moquin-Beaudry, Pierre Khneisser, Maria Eugenia Marques Da Costa, Slim Karkar, Hanane Boudhouche, Damien Drubay, Baptiste Audinot, Birgit Geoerger, Jean-Yves Scoazec, Nathalie Gaspar, Antonin Marchais","doi":"10.1093/bioadv/vbae081","DOIUrl":"10.1093/bioadv/vbae081","url":null,"abstract":"<p><strong>Motivation: </strong>Spatial transcriptomics enables the analysis of cell crosstalk in healthy and diseased organs by capturing the transcriptomic profiles of millions of cells within their spatial contexts. However, spatial transcriptomics approaches also raise new computational challenges for the multidimensional data analysis associated with spatial coordinates.</p><p><strong>Results: </strong>In this context, we introduce a novel analytical framework called CellsFromSpace based on independent component analysis (ICA), which allows users to analyze various commercially available technologies without relying on a single-cell reference dataset. The ICA approach deployed in CellsFromSpace decomposes spatial transcriptomics data into interpretable components associated with distinct cell types or activities. ICA also enables noise or artifact reduction and subset analysis of cell types of interest through component selection. We demonstrate the flexibility and performance of CellsFromSpace using real-world samples to demonstrate ICA's ability to successfully identify spatially distributed cells as well as rare diffuse cells, and quantitatively deconvolute datasets from the Visium, Slide-seq, MERSCOPE, and CosMX technologies. Comparative analysis with a current alternative reference-free deconvolution tool also highlights CellsFromSpace's speed, scalability and accuracy in processing complex, even multisample datasets. CellsFromSpace also offers a user-friendly graphical interface enabling non-bioinformaticians to annotate and interpret components based on spatial distribution and contributor genes, and perform full downstream analysis.</p><p><strong>Availability and implementation: </strong>CellsFromSpace (CFS) is distributed as an R package available from github at https://github.com/gustaveroussy/CFS along with tutorials, examples, and detailed documentation.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11194756/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of tumor-specific splicing from somatic mutations as a source of neoantigen candidates.","authors":"Franziska Lang, Patrick Sorn, Martin Suchan, Alina Henrich, Christian Albrecht, Nina Köhl, Aline Beicht, Pablo Riesgo-Ferreiro, Christoph Holtsträter, Barbara Schrörs, David Weber, Martin Löwer, Ugur Sahin, Jonas Ibn-Salem","doi":"10.1093/bioadv/vbae080","DOIUrl":"10.1093/bioadv/vbae080","url":null,"abstract":"<p><strong>Motivation: </strong>Neoantigens are promising targets for cancer immunotherapies and might arise from alternative splicing. However, detecting tumor-specific splicing is challenging because many non-canonical splice junctions identified in tumors also appear in healthy tissues. To increase tumor-specificity, we focused on splicing caused by somatic mutations as a source for neoantigen candidates in individual patients.</p><p><strong>Results: </strong>We developed the tool splice2neo with multiple functionalities to integrate predicted splice effects from somatic mutations with splice junctions detected in tumor RNA-seq and to annotate the resulting transcript and peptide sequences. Additionally, we provide the tool EasyQuant for targeted RNA-seq read mapping to candidate splice junctions. Using a stringent detection rule, we predicted 1.7 splice junctions per patient as splice targets with a false discovery rate below 5% in a melanoma cohort. We confirmed tumor-specificity using independent, healthy tissue samples. Furthermore, using tumor-derived RNA, we confirmed individual exon-skipping events experimentally. Most target splice junctions encoded neoepitope candidates with predicted major histocompatibility complex (MHC)-I or MHC-II binding. Compared to neoepitope candidates from non-synonymous point mutations, the splicing-derived MHC-I neoepitope candidates had lower self-similarity to corresponding wild-type peptides. In conclusion, we demonstrate that identifying mutation-derived, tumor-specific splice junctions can lead to additional neoantigen candidates to expand the target repertoire for cancer immunotherapies.</p><p><strong>Availability and implementation: </strong>The R package splice2neo and the python package EasyQuant are available at https://github.com/TRON-Bioinformatics/splice2neo and https://github.com/TRON-Bioinformatics/easyquant, respectively.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11165244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141307454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncover spatially informed variations for single-cell spatial transcriptomics with STew.","authors":"Nanxi Guo, Juan Vargas, Samantha Reynoso, Douglas Fritz, Revanth Krishna, Chuangqi Wang, Fan Zhang","doi":"10.1093/bioadv/vbae064","DOIUrl":"10.1093/bioadv/vbae064","url":null,"abstract":"<p><strong>Motivation: </strong>The recent spatial transcriptomics (ST) technologies have enabled characterization of gene expression patterns and spatial information, advancing our understanding of cell lineages within diseased tissues. Several analytical approaches have been proposed for ST data, but effectively utilizing spatial information to unveil the shared variation with gene expression remains a challenge.</p><p><strong>Results: </strong>We introduce STew, a Spatial Transcriptomic multi-viEW representation learning method, to jointly analyze spatial information and gene expression in a scalable manner, followed by a data-driven statistical framework to measure the goodness of model fit. Through benchmarking using human dorsolateral prefrontal cortex and mouse main olfactory bulb data with true manual annotations, STew achieved superior performance in both clustering accuracy and continuity of identified spatial domains compared with other methods. STew is also robust to generate consistent results insensitive to model parameters, including sparsity constraints. We next applied STew to various ST data acquired from 10× Visium, Slide-seqV2, and 10× Xenium, encompassing single-cell and multi-cellular resolution ST technologies, which revealed spatially informed cell type clusters and biologically meaningful axes. In particular, we identified a proinflammatory fibroblast spatial niche using ST data from psoriatic skins. Moreover, STew scales almost linearly with the number of spatial locations, guaranteeing its applicability to datasets with thousands of spatial locations to capture disease-relevant niches in complex tissues.</p><p><strong>Availability and implementation: </strong>Source code and the R software tool STew are available from github.com/fanzhanglab/STew.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11142628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141201392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improve-RRBS: a novel tool to correct the 3’ trimming of reduced representation sequencing reads","authors":"Abel Fothi, Hongbo Liu, Katalin Susztak, Tamás Arányi","doi":"10.1093/bioadv/vbae076","DOIUrl":"https://doi.org/10.1093/bioadv/vbae076","url":null,"abstract":"\u0000 \u0000 \u0000 Reduced Representation Bisulfite Sequencing (RRBS) is a popular approach to determine DNA methylation of the CpG-rich regions of the genome. However, we observed that false positive differentially methylated sites (DMS) are also identified using the standard computational analysis.\u0000 \u0000 \u0000 \u0000 During RRBS library preparation the MspI digested DNA undergo end-repair by a cytosine at the 3’ end of the fragments. After sequencing, Trim Galore cuts these end-repaired nucleotides. However, Trim Galore fails to detect end-repair when it overlaps with the 3’ end of the sequencing reads. We found that these non-trimmed cytosines bias methylation calling, thus can identify DMS erroneously. To circumvent this problem, we developed improve-RRBS, which efficiently identifies and hides these cytosines from methylation calling with a false positive rate of maximum 0.5%. To test improve-RRBS, we investigated four datasets from four laboratories and two different species. We found non-trimmed 3’ cytosines in all datasets analyzed and as much as > 50% of false positive DMS under certain conditions. By applying improve-RRBS, these DMS completely disappeared from all comparisons.\u0000 \u0000 \u0000 \u0000 improve-RRBS is a freely available python package https://pypi.org/project/iRRBS/ or https://github.com/fothia/improve-RRBS to be implemented in RRBS pipelines.\u0000 \u0000 \u0000 \u0000 Supplementary data are available at Bioinformatics Advances online.\u0000","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141099680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predmoter-cross-species prediction of plant promoter and enhancer regions.","authors":"Felicitas Kindel, Sebastian Triesch, Urte Schlüter, Laura Alexandra Randarevitch, Vanessa Reichel-Deland, Andreas P M Weber, Alisandra K Denton","doi":"10.1093/bioadv/vbae074","DOIUrl":"10.1093/bioadv/vbae074","url":null,"abstract":"<p><strong>Motivation: </strong>Identifying <i>cis</i>-regulatory elements (CREs) is crucial for analyzing gene regulatory networks. Next generation sequencing methods were developed to identify CREs but represent a considerable expenditure for targeted analysis of few genomic loci. Thus, predicting the outputs of these methods would significantly cut costs and time investment.</p><p><strong>Results: </strong>We present Predmoter, a deep neural network that predicts base-wise Assay for Transposase Accessible Chromatin using sequencing (ATAC-seq) and histone Chromatin immunoprecipitation DNA-sequencing (ChIP-seq) read coverage for plant genomes. Predmoter uses only the DNA sequence as input. We trained our final model on 21 species for 13 of which ATAC-seq data and for 17 of which ChIP-seq data was publicly available. We evaluated our models on <i>Arabidopsis thaliana</i> and <i>Oryza sativa</i>. Our best models showed accurate predictions in peak position and pattern for ATAC- and histone ChIP-seq. Annotating putatively accessible chromatin regions provides valuable input for the identification of CREs. In conjunction with other <i>in silico</i> data, this can significantly reduce the search space for experimentally verifiable DNA-protein interaction pairs.</p><p><strong>Availability and implementation: </strong>The source code for Predmoter is available at: https://github.com/weberlab-hhu/Predmoter. Predmoter takes a fasta file as input and outputs h5, and optionally bigWig and bedGraph files.</p>","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11150885/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141263386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"WGCCRR: a web-based tool for genome-wide screening of convergent indels and substitutions of amino-acids","authors":"Zheng Dong, Chen Wang, Qingming Qu","doi":"10.1093/bioadv/vbae070","DOIUrl":"https://doi.org/10.1093/bioadv/vbae070","url":null,"abstract":"\u0000 Genome-wide analyses of protein-coding gene sequences are being employed to examine the genetic basis of adaptive evolution in many organismal groups. Previous studies have revealed that convergent/parallel adaptive evolution may be caused by convergent/parallel amino acid changes. Similarly, detailed analysis of lineage-specific amino acid changes has shown correlations with certain lineage-specific traits. However, experimental validation remains the ultimate measure of causality. With the increasing availability of genomic data, a streamlined tool for such analyses would facilitate and expedite the screening of genetic loci that hold potential for adaptive evolution, while alleviating the bioinformatic burden for experimental biologists. In this study, we present a user-friendly web-based tool called WGCCRR (Whole Genome Comparative Coding Region Read) designed to screen both convergent/parallel and lineage-specific amino acid changes on a genome-wide scale. Our tool allows users to replicate previous analyses with just a few clicks, and the exported results are straightforward to interpret. In addition, we have also included amino acid indels that are usually neglected in previous work. Our website provides an efficient platform for screening candidate loci for downstream experimental tests. It is available at: https://fishevo.xmu.edu.cn/.","PeriodicalId":72368,"journal":{"name":"Bioinformatics advances","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141101376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}