{"entity": "journal", "iuid": "ac6fa033a0ce401ea0de8aee83710e58", "timestamp": "2026-08-17T02:27:04.972Z", "links": {"self": {"href": "https://publications.scilifelab.se/journal/Brief.%20Bioinformatics.json"}, "display": {"href": "https://publications.scilifelab.se/journal/Brief.%20Bioinformatics"}}, "title": "Brief. Bioinformatics", "issn": "1477-4054", "issn-l": "1467-5463", "publications_count": 3, "publications": [{"entity": "publication", "iuid": "5a0fd3eeab8e4303ade4bc3356923079", "links": {"self": {"href": "https://publications.scilifelab.se/publication/5a0fd3eeab8e4303ade4bc3356923079.json"}, "display": {"href": "https://publications.scilifelab.se/publication/5a0fd3eeab8e4303ade4bc3356923079"}}, "title": "Tracing the evolution of aneuploid cancers by multiregional sequencing with CRUST.", "authors": [{"family": "Chattopadhyay", "given": "Subhayan", "initials": "S", "orcid": "0000-0002-8599-2971", "researcher": {"href": "https://publications.scilifelab.se/researcher/78358668578b4661bed1f6a37365fae4.json"}}, {"family": "Karlsson", "given": "Jenny", "initials": "J"}, {"family": "Valind", "given": "Anders", "initials": "A"}, {"family": "Andersson", "given": "Natalie", "initials": "N"}, {"family": "Gisselsson", "given": "David", "initials": "D"}], "type": "journal article", "published": "2021-11-05", "journal": {"title": "Brief. Bioinformatics", "issn": "1477-4054", "volume": "22", "issue": "6", "issn-l": "1467-5463"}, "abstract": "Clonal deconvolution of mutational landscapes is crucial to understand the evolutionary dynamics of cancer. Two limiting factors for clonal deconvolution that have remained unresolved are variation in purity and chromosomal copy number across different samples of the same tumor. We developed a semi-supervised algorithm that tracks variant calls through multi-sample spatiotemporal tumor data. While normalizing allele frequencies based on purity, it also adjusts for copy number changes at clonal deconvolution. Absent \u00e0 priori copy number data, it renders in silico copy number estimations from bulk sequences. Using published and simulated tumor sequences, we reliably segregated clonal/subclonal variants even at a low sequencing depth (~50\u00d7). Given at least one pure tumor sample (>70% purity), we could normalize and deconvolve paired samples down to a purity of 40%. This renders a reliable clonal reconstruction well adapted to multi-regionally sampled solid tumors, which are often aneuploid and contaminated by non-cancer cells.", "doi": "10.1093/bib/bbab292", "pmid": "34343239", "labels": {"National Genomics Infrastructure": "Service", "NGI Stockholm (Genomics Production)": "Service", "NGI Stockholm (Genomics Applications)": "Service"}, "xrefs": [{"db": "pii", "key": "6337895"}], "notes": [], "created": "2021-10-01T09:03:47.437Z", "modified": "2021-12-06T13:48:08.580Z"}, {"entity": "publication", "iuid": "5de5640268cd4fc898e4ecf7288712c3", "links": {"self": {"href": "https://publications.scilifelab.se/publication/5de5640268cd4fc898e4ecf7288712c3.json"}, "display": {"href": "https://publications.scilifelab.se/publication/5de5640268cd4fc898e4ecf7288712c3"}}, "title": "Computational pan-genomics: status, promises and challenges.", "authors": [{"family": "Computational Pan-Genomics Consortium", "given": null, "initials": null}], "type": "journal article", "published": "2016-10-21", "journal": {"volume": null, "issn": "1477-4054", "issue": null, "title": "Brief. Bioinformatics", "issn-l": "1467-5463"}, "abstract": "Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.", "doi": "10.1093/bib/bbw089", "pmid": "27769991", "labels": {"Bioinformatics Support, Infrastructure and Training": "Technology development", "Bioinformatics Long-term Support WABI": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [{"db": "pii", "key": "bbw089"}], "notes": [], "created": "2017-05-03T13:00:34.772Z", "modified": "2020-01-21T13:53:21.113Z"}, {"entity": "publication", "iuid": "c9c9bbbce81e48a9925df83187c4119f", "links": {"self": {"href": "https://publications.scilifelab.se/publication/c9c9bbbce81e48a9925df83187c4119f.json"}, "display": {"href": "https://publications.scilifelab.se/publication/c9c9bbbce81e48a9925df83187c4119f"}}, "title": "Assessing the consistency of public human tissue RNA-seq data sets.", "authors": [{"family": "Danielsson", "given": "Frida", "initials": "F"}, {"family": "James", "given": "Tojo", "initials": "T"}, {"family": "Gomez-Cabrero", "given": "David", "initials": "D"}, {"family": "Huss", "given": "Mikael", "initials": "M"}], "type": "journal article", "published": "2015-11-00", "journal": {"volume": "16", "issn": "1477-4054", "issue": "6", "pages": "941-949", "title": "Brief. Bioinformatics", "issn-l": "1467-5463"}, "abstract": "Sequencing-based gene expression methods like RNA-sequencing (RNA-seq) have become increasingly common, but it is often claimed that results obtained in different studies are not comparable owing to the influence of laboratory batch effects, differences in RNA extraction and sequencing library preparation methods and bioinformatics processing pipelines. It would be unfortunate if different experiments were in fact incomparable, as there is great promise in data fusion and meta-analysis applied to sequencing data sets. We therefore compared reported gene expression measurements for ostensibly similar samples (specifically, human brain, heart and kidney samples) in several different RNA-seq studies to assess their overall consistency and to examine the factors contributing most to systematic differences. The same comparisons were also performed after preprocessing all data in a consistent way, eliminating potential bias from bioinformatics pipelines. We conclude that published human tissue RNA-seq expression measurements appear relatively consistent in the sense that samples cluster by tissue rather than laboratory of origin given simple preprocessing transformations. The article is supplemented by a detailed walkthrough with embedded R code and figures.", "doi": "10.1093/bib/bbv017", "pmid": "25829468", "labels": {"Bioinformatics Support, Infrastructure and Training": "Technology development", "Bioinformatics Long-term Support WABI": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [{"db": "pii", "key": "bbv017"}, {"db": "pmc", "key": "PMC4652619"}], "notes": [], "created": "2017-05-02T12:56:48.590Z", "modified": "2020-01-21T13:53:21.183Z"}], "created": "2017-05-09T09:12:07.537Z", "modified": "2020-11-27T13:14:04.066Z"}