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Dag G. Ahrén

dblp:26/5607 · DBLP profile ↗
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5ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 5 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
2 papers
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › genomics
plant genomics
0.212016
NBSPred: a support vector machine-based high-throughput pipeline for plant resistance protein NBSLRR prediction · Bioinform. 2016
Bioinformatics and computational biology › protein function prediction
protein classification
0.212016
NBSPred: a support vector machine-based high-throughput pipeline for plant resistance protein NBSLRR prediction · Bioinform. 2016
Bioinformatics and computational biology
comparative genomics
0.112005
CoGenT++: an extensive and extensible data environment for computational genomics · Bioinform. 2005
Bioinformatics and computational biology
functional genomics
0.112005
CoGenT++: an extensive and extensible data environment for computational genomics · Bioinform. 2005

Methods — techniques the papers use, named apart from their topics

support vector machine · 0.2phylogenetic profiling · 0.1all-against-all similarity · 0.1
YearPublicationVenuePosition
2023 CGG toolkit: Software components for computational genomics
abstract
Public-domain availability for bioinformatics software resources is a key requirement that ensures long-term permanence and methodological reproducibility for research and development across the life sciences. These issues are particularly critical for widely used, efficient, and well-proven methods, especially those developed in research settings that often face funding discontinuities. We re-launch a range of established software components for computational genomics, as legacy version 1.0.1, suitable for sequence matching, masking, searching, clustering and visualization for protein family discovery, annotation and functional characterization on a genome scale. These applications are made available online as open source and include MagicMatch, GeneCAST, support scripts for CoGenT-like sequence collections, GeneRAGE and DifFuse, supported by centrally administered bioinformatics infrastructure funding. The toolkit may also be conceived as a flexible genome comparison software pipeline that supports research in this domain. We illustrate basic use by examples and pictorial representations of the registered tools, which are further described with appropriate documentation files in the corresponding GitHub release.
Dimitrios Vasileiou, Christos Karapiperis, Ismini Baltsavia, Anastasia Chasapi, Dag G. Ahrén, Paul J. Janssen, Vasilis J. Promponas, Anton J. Enright, Christos A. Ouzounis
PLoS Comput. Biol.5
2016 NBSPred: a support vector machine-based high-throughput pipeline for plant resistance protein NBSLRR prediction
abstract
UNLABELLED: The nucleotide binding site leucine-rich repeats (NBSLRRs) belong to one of the largest known families of disease resistance genes that encode resistance proteins (R-protein) against the pathogens of plants. Various defence mechanisms have explained the regulation of plant immunity, but still, we have limited understanding about plant defence against different pathogens. Identification of R-proteins and proteins having R-protein-like features across the genome, transcriptome and proteome would be highly useful to develop the global understanding of plant defence mechanisms, but it is laborious and time-consuming task. Therefore, we have developed a support vector machine-based high-throughput pipeline called NBSPred to differentiate NBSLRR and NBSLRR-like protein from Non-NBSLRR proteins from genome, transcriptome and protein sequences. The pipeline was tested and validated with input sequences from three dicot and two monocot plants including Arabidopsis thaliana, Boechera stricta, Brachypodium distachyon Solanum lycopersicum and Zea mays. AVAILABILITY AND IMPLEMENTATION: The NBSPred pipeline is available at http://soilecology.biol.lu.se/nbs/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: [email protected].
Sandeep Kumar Kushwaha, Pallavi Chauhan, Katarina Hedlund, Dag G. Ahrén
Bioinform.4
2016 Erratum to: MetCap: A bioinformatics probe design pipeline for large-scale targeted metagenomics
abstract
Results), page 1 To illustrate the advantage of a targeted metagenome ap- proach, we have generated more than 400,000 probes that match more than 300,000 publicly available sequences related to carbon degradation, and used these probes for targeted sequencing in a soil metagenome study.
Sandeep Kumar Kushwaha, Lokeshwaran Manoharan, Tejashwari Meerupati, Katarina Hedlund, Dag G. Ahrén
BMC Bioinform.5
2015 MetCap: a bioinformatics probe design pipeline for large-scale targeted metagenomics
abstract
BACKGROUND: Massive sequencing of genes from different environments has evolved metagenomics as central to enhancing the understanding of the wide diversity of micro-organisms and their roles in driving ecological processes. Reduced cost and high throughput sequencing has made large-scale projects achievable to a wider group of researchers, though complete metagenome sequencing is still a daunting task in terms of sequencing as well as the downstream bioinformatics analyses. Alternative approaches such as targeted amplicon sequencing requires custom PCR primer generation, and is not scalable to thousands of genes or gene families. RESULTS: In this study, we are presenting a web-based tool called MetCap that circumvents the limitations of amplicon sequencing of multiple genes by designing probes that are suitable for large-scale targeted metagenomics sequencing studies. MetCap provides a novel approach to target thousands of genes and genomic regions that could be used in targeted metagenomics studies. Automatic analysis of user-defined sequences is performed, and probes specifically designed for metagenome studies are generated. To illustrate the advantage of a targeted metagenome approach, we have generated more than 400,000 probes that match more than 300,000 [corrected] publicly available sequences related to carbon degradation, and used these probes for target sequencing in a soil metagenome study. The results show high enrichment of target genes and a successful capturing of the majority of gene families. MetCap is freely available to users from: http://soilecology.biol.lu.se/metcap/ . CONCLUSION: MetCap is facilitating probe-based target enrichment as an easy and efficient alternative tool compared to complex primer-based enrichment for large-scale investigations of metagenomes. Our results have shown efficient large-scale target enrichment through MetCap-designed probes for a soil metagenome. The web service is suitable for any targeted metagenomics project that aims to study several genes simultaneously. The novel bioinformatics approach taken by the web service will enable researchers in microbial ecology to tap into the vast diversity of microbial communities using targeted metagenomics as a cost-effective alternative to whole metagenome sequencing.
Sandeep Kumar Kushwaha, Lokeshwaran Manoharan, Tejashwari Meerupati, Katarina Hedlund, Dag G. Ahrén
BMC Bioinform.5
2005 CoGenT++: an extensive and extensible data environment for computational genomics
abstract
MOTIVATION: CoGenT++ is a data environment for computational research in comparative and functional genomics, designed to address issues of consistency, reproducibility, scalability and accessibility. DESCRIPTION: CoGenT++ facilitates the re-distribution of all fully sequenced and published genomes, storing information about species, gene names and protein sequences. We describe our scalable implementation of ProXSim, a continually updated all-against-all similarity database, which stores pairwise relationships between all genome sequences. Based on these similarities, derived databases are generated for gene fusions--AllFuse, putative orthologs--OFAM, protein families--TRIBES, phylogenetic profiles--ProfUse and phylogenetic trees. Extensions based on the CoGenT++ environment include disease gene prediction, pattern discovery, automated domain detection, genome annotation and ancestral reconstruction. CONCLUSION: CoGenT++ provides a comprehensive environment for computational genomics, accessible primarily for large-scale analyses as well as manual browsing.
Leon Goldovsky, Paul J. Janssen, Dag G. Ahrén, Benjamin Audit, Ildefonso Cases, Nikos Darzentas, Anton J. Enright, Núria López-Bigas, José M. Peregrín-Alvarez, Mike Smith 0001, Sophia Tsoka, Victor Kunin, Christos A. Ouzounis
Bioinform.3