NAR Genomics and Bioinformatics最新文献_第10页

eQTL-Detect: nextflow-based pipeline for eQTL detection in modular format with sharable and parallelizable scripts. eQTL-Detect：基于 nextflow 的 eQTL 检测管道，采用模块化格式，脚本可共享和并行。

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NAR Genomics and Bioinformatics Pub Date : 2024-09-24 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae122

Praveen Krishna Chitneedi, Frieder Hadlich, Gabriel C M Moreira, Jose Espinosa-Carrasco, Changxi Li, Graham Plastow, Daniel Fischer, Carole Charlier, Dominique Rocha, Amanda J Chamberlain, Christa Kuehn

引用次数: 0

GenomicLinks: deep learning predictions of 3D chromatin interactions in the maize genome. GenomicLinks：玉米基因组中三维染色质相互作用的深度学习预测。

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NAR Genomics and Bioinformatics Pub Date : 2024-09-24 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae123

Luca Schlegel, Rohan Bhardwaj, Yadollah Shahryary, Defne Demirtürk, Alexandre P Marand, Robert J Schmitz, Frank Johannes

{"title":"GenomicLinks: deep learning predictions of 3D chromatin interactions in the maize genome.","authors":"Luca Schlegel, Rohan Bhardwaj, Yadollah Shahryary, Defne Demirtürk, Alexandre P Marand, Robert J Schmitz, Frank Johannes","doi":"10.1093/nargab/lqae123","DOIUrl":"10.1093/nargab/lqae123","url":null,"abstract":"Gene regulation in eukaryotes is partly shaped by the 3D organization of chromatin within the cell nucleus. Distal interactions between cis-regulatory elements and their target genes are widespread, and many causal loci underlying heritable agricultural traits have been mapped to distal non-coding elements. The biology underlying chromatin loop formation in plants is poorly understood. Dissecting the sequence features that mediate distal interactions is an important step toward identifying putative molecular mechanisms. Here, we trained GenomicLinks, a deep learning model, to identify DNA sequence features predictive of 3D chromatin interactions in maize. We found that the presence of binding motifs of specific transcription factor classes, especially bHLH, is predictive of chromatin interaction specificities. Using an in silico mutagenesis approach we show the removal of these motifs from loop anchors leads to reduced interaction probabilities. We were able to validate these predictions with single-cell co-accessibility data from different maize genotypes that harbor natural substitutions in these TF binding motifs. GenomicLinks is currently implemented as an open-source web tool, which should facilitate its wider use in the plant research community.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"6 3","pages":"lqae123"},"PeriodicalIF":4.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11420838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355521","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}

引用次数: 0

Effective requesting method to detect fusion transcripts in chronic myelomonocytic leukemia RNA-seq. 在慢性粒细胞白血病 RNA-seq 中检测融合转录本的有效请求方法。

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NAR Genomics and Bioinformatics Pub Date : 2024-09-24 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae117

Florence Rufflé, Jérôme Reboul, Anthony Boureux, Benoit Guibert, Chloé Bessière, Raissa Silva, Eric Jourdan, Jean-Baptiste Gaillard, Anne Boland, Jean-François Deleuze, Catherine Sénamaud-Beaufort, Dorothée Selimoglu-Buet, Eric Solary, Nicolas Gilbert, Thérèse Commes

{"title":"Effective requesting method to detect fusion transcripts in chronic myelomonocytic leukemia RNA-seq.","authors":"Florence Rufflé, Jérôme Reboul, Anthony Boureux, Benoit Guibert, Chloé Bessière, Raissa Silva, Eric Jourdan, Jean-Baptiste Gaillard, Anne Boland, Jean-François Deleuze, Catherine Sénamaud-Beaufort, Dorothée Selimoglu-Buet, Eric Solary, Nicolas Gilbert, Thérèse Commes","doi":"10.1093/nargab/lqae117","DOIUrl":"https://doi.org/10.1093/nargab/lqae117","url":null,"abstract":"RNA sequencing technology combining short read and long read analysis can be used to detect chimeric RNAs in malignant cells. Here, we propose an integrated approach that uses k-mers to analyze indexed datasets. This approach is used to identify chimeric RNA in chronic myelomonocytic leukemia (CMML) cells, a myeloid malignancy that associates features of myelodysplastic and myeloproliferative neoplasms. In virtually every CMML patient, new generation sequencing identifies one or several somatic driver mutations, typically affecting epigenetic, splicing and signaling genes. In contrast, cytogenetic aberrations are currently detected in only one third of the cases. Nevertheless, chromosomal abnormalities contribute to patient stratification, some of them being associated with higher risk of poor outcome, e.g. through transformation into acute myeloid leukemia (AML). Our approach selects four chimeric RNAs that have been detected and validated in CMML cells. We further focus on NRIP1-MIR99AHG, as this fusion has also recently been detected in AML cells. We show that this fusion encodes three isoforms, including a novel one. Further studies will decipher the biological significance of such a fusion and its potential to improve disease stratification. Taken together, this report demonstrates the ability of a large-scale approach to detect chimeric RNAs in cancer cells.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"6 3","pages":"lqae117"},"PeriodicalIF":4.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11420675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142355518","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}

引用次数: 0

Comparative analysis of single-cell pathway scoring methods and a novel approach. 单细胞通路评分方法和一种新方法的比较分析。

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NAR Genomics and Bioinformatics Pub Date : 2024-09-24 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae124

Ruoqiao H Wang, Juilee Thakar

引用次数: 0

A generalized protein identification method for novel and diverse sequencing technologies. 适用于新型和多样化测序技术的通用蛋白质识别方法。

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NAR Genomics and Bioinformatics Pub Date : 2024-09-18 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae126

Bikash Kumar Bhandari, Nick Goldman

{"title":"A generalized protein identification method for novel and diverse sequencing technologies.","authors":"Bikash Kumar Bhandari, Nick Goldman","doi":"10.1093/nargab/lqae126","DOIUrl":"https://doi.org/10.1093/nargab/lqae126","url":null,"abstract":"Protein sequencing is a rapidly evolving field with much progress towards the realization of a new generation of protein sequencers. The early devices, however, may not be able to reliably discriminate all 20 amino acids, resulting in a partial, noisy and possibly error-prone signature of a protein. Rather than achieving de novo sequencing, these devices may aim to identify target proteins by comparing such signatures to databases of known proteins. However, there are no broadly applicable methods for this identification problem. Here, we devise a hidden Markov model method to study the generalized problem of protein identification from noisy signature data. Based on a hypothetical sequencing device that can simulate several novel technologies, we show that on the human protein database (N = 20 181) our method has a good performance under many different operating conditions such as various levels of signal resolvability, different numbers of discriminated amino acids, sequence fragments, and insertion and deletion error rates. Our results demonstrate the possibility of protein identification with high accuracy on many early experimental devices. We anticipate our method to be applicable for a wide range of protein sequencing devices in the future.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"6 3","pages":"lqae126"},"PeriodicalIF":4.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11409062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142297107","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}

引用次数: 0

Evaluation of machine learning models that predict lncRNA subcellular localization. 评估预测 lncRNA 亚细胞定位的机器学习模型。

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NAR Genomics and Bioinformatics Pub Date : 2024-09-18 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae125

Jason R Miller, Weijun Yi, Donald A Adjeroh

{"title":"Evaluation of machine learning models that predict lncRNA subcellular localization.","authors":"Jason R Miller, Weijun Yi, Donald A Adjeroh","doi":"10.1093/nargab/lqae125","DOIUrl":"https://doi.org/10.1093/nargab/lqae125","url":null,"abstract":"The lncATLAS database quantifies the relative cytoplasmic versus nuclear abundance of long non-coding RNAs (lncRNAs) observed in 15 human cell lines. The literature describes several machine learning models trained and evaluated on these and similar datasets. These reports showed moderate performance, e.g. 72-74% accuracy, on test subsets of the data withheld from training. In all these reports, the datasets were filtered to include genes with extreme values while excluding genes with values in the middle range and the filters were applied prior to partitioning the data into training and testing subsets. Using several models and lncATLAS data, we show that this 'middle exclusion' protocol boosts performance metrics without boosting model performance on unfiltered test data. We show that various models achieve only about 60% accuracy when evaluated on unfiltered lncRNA data. We suggest that the problem of predicting lncRNA subcellular localization from nucleotide sequences is more challenging than currently perceived. We provide a basic model and evaluation procedure as a benchmark for future studies of this problem.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"6 3","pages":"lqae125"},"PeriodicalIF":4.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11409063/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142297109","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}

引用次数: 0

GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell data. GeneSPIDER2：利用扰动单细胞数据进行大规模 GRN 模拟和基准测试。

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NAR Genomics and Bioinformatics Pub Date : 2024-09-18 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae121

Mateusz Garbulowski, Thomas Hillerton, Daniel Morgan, Deniz Seçilmiş, Lisbet Sonnhammer, Andreas Tjärnberg, Torbjörn E M Nordling, Erik L L Sonnhammer

{"title":"GeneSPIDER2: large scale GRN simulation and benchmarking with perturbed single-cell data.","authors":"Mateusz Garbulowski, Thomas Hillerton, Daniel Morgan, Deniz Seçilmiş, Lisbet Sonnhammer, Andreas Tjärnberg, Torbjörn E M Nordling, Erik L L Sonnhammer","doi":"10.1093/nargab/lqae121","DOIUrl":"https://doi.org/10.1093/nargab/lqae121","url":null,"abstract":"Single-cell data is increasingly used for gene regulatory network (GRN) inference, and benchmarks for this have been developed based on simulated data. However, existing single-cell simulators cannot model the effects of gene perturbations. A further challenge lies in generating large-scale GRNs that often struggle with computational and stability issues. We present GeneSPIDER2, an update of the GeneSPIDER MATLAB toolbox for GRN benchmarking, inference, and analysis. Several software modules have improved capabilities and performance, and new functionalities have been added. A major improvement is the ability to generate large GRNs with biologically realistic topological properties in terms of scale-free degree distribution and modularity. Another major addition is a simulation of single-cell data, which is becoming increasingly popular as input for GRN inference. Specifically, we introduced the unique feature to generate single-cell data based on genetic perturbations. Finally, the simulated single-cell data was compared to real single-cell Perturb-seq data from two cell lines, showing that the synthetic and real data exhibit similar properties.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"6 3","pages":"lqae121"},"PeriodicalIF":4.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11409065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142297111","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}

引用次数: 0

Transformer model generated bacteriophage genomes are compositionally distinct from natural sequences. 变压器模型生成的噬菌体基因组在组成上有别于天然序列。

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NAR Genomics and Bioinformatics Pub Date : 2024-09-18 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae129

Jeremy Ratcliff

{"title":"Transformer model generated bacteriophage genomes are compositionally distinct from natural sequences.","authors":"Jeremy Ratcliff","doi":"10.1093/nargab/lqae129","DOIUrl":"https://doi.org/10.1093/nargab/lqae129","url":null,"abstract":"Novel applications of language models in genomics promise to have a large impact on the field. The megaDNA model is the first publicly available generative model for creating synthetic viral genomes. To evaluate megaDNA's ability to recapitulate the nonrandom genome composition of viruses and assess whether synthetic genomes can be algorithmically detected, compositional metrics for 4969 natural bacteriophage genomes and 1002 de novo synthetic bacteriophage genomes were compared. Transformer-generated sequences had varied but realistic genome lengths, and 58% were classified as viral by geNomad. However, the sequences demonstrated consistent differences in various compositional metrics when compared to natural bacteriophage genomes by rank-sum tests and principal component analyses. A simple neural network trained to detect transformer-generated sequences on global compositional metrics alone displayed a median sensitivity of 93.0% and specificity of 97.9% (n = 12 independent models). Overall, these results demonstrate that megaDNA does not yet generate bacteriophage genomes with realistic compositional biases and that genome composition is a reliable method for detecting sequences generated by this model. While the results are specific to the megaDNA model, the evaluated framework described here could be applied to any generative model for genomic sequences.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"6 3","pages":"lqae129"},"PeriodicalIF":4.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11409064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142297112","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}

引用次数: 0

Correction to 'long non-coding RNAs involved in Drosophila development and regeneration'. 参与果蝇发育和再生的长非编码 RNAs "的更正。

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NAR Genomics and Bioinformatics Pub Date : 2024-09-14 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae127

引用次数: 0

FlaHMM: unistrand flamenco-like piRNA cluster prediction in Drosophila species using hidden Markov models. FlaHMM：利用隐马尔可夫模型预测果蝇物种中的单链弗拉门戈式 piRNA 簇。

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NAR Genomics and Bioinformatics Pub Date : 2024-09-14 eCollection Date: 2024-09-01 DOI: 10.1093/nargab/lqae119

Maria-Anna Trapotsi, Jasper van Lopik, Gregory J Hannon, Benjamin Czech Nicholson, Susanne Bornelöv

{"title":"FlaHMM: unistrand flamenco-like piRNA cluster prediction in Drosophila species using hidden Markov models.","authors":"Maria-Anna Trapotsi, Jasper van Lopik, Gregory J Hannon, Benjamin Czech Nicholson, Susanne Bornelöv","doi":"10.1093/nargab/lqae119","DOIUrl":"10.1093/nargab/lqae119","url":null,"abstract":"PIWI-interacting RNAs (piRNAs) are a class of small non-coding RNAs that are essential for transposon control in animal gonads. In Drosophila ovarian somatic cells, piRNAs are transcribed from large genomic regions called piRNA clusters, which are enriched for transposon fragments and act as a memory of past invasions. Despite being widely present across Drosophila species, somatic piRNA clusters are difficult to identify and study due to their lack of sequence conservation and limited synteny. Current identification methods rely on either extensive manual curation or availability of high-throughput small RNA sequencing data, limiting large-scale comparative studies. We now present FlaHMM, a hidden Markov model developed to automate genomic annotation of flamenco-like unistrand piRNA clusters in Drosophila species, requiring only a genome assembly and transposon annotations. FlaHMM uses transposable element content across 5- or 10-kb bins, which can be calculated from genome sequence alone, and is thus able to detect candidate piRNA clusters without the need to obtain flies and experimentally perform small RNA sequencing. We show that FlaHMM performs on par with piRNA-guided or manual methods, and thus provides a scalable and efficient approach to piRNA cluster annotation in new genome assemblies. FlaHMM is freely available at https://github.com/Hannon-lab/FlaHMM under an MIT licence.","PeriodicalId":33994,"journal":{"name":"NAR Genomics and Bioinformatics","volume":"6 3","pages":"lqae119"},"PeriodicalIF":4.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11400887/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142297110","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}

引用次数: 0