Research Methods

The RICOPILI pipeline for rapid imputation of consortia-scale genetic association data The RICOPILI pipeline is an open-sourced, Perl-based system that allows the efficient quality control, imputation, and analysis of genome-wide association data from consortia-scle multi-cohort studies. Links to the manuscript, script and tutorials are provided in the links. Citation: Max Lam, Swapnil Awasthi, Hunna J Watson, et al., RICOPILI: Rapid Imputation for COnsortias PIpeLIne, Bioinformatics, Volume 36, Issue 3, 1 February 2020, Pages 930933, https://doi.org/10.1093/bioinformatics/btz633 Natural science - Biology - Genetics - Data imputation Posted by team TransBio from: Zentralinstitut für Seelische Gesundheit

3

Sequential Bayesian Randomized Controlled Trial Bayesian sequential analysis design that allows for the early identification of likely effective or clearly ineffective treatment protocols in randomized controlled trials. Applied science - Medicine and health - Psychiatry - Clinical Trials Posted by team CNBS-MA from: Ludwig-Maximilians-Universität München

1

Extensive Tool for Network Analysis of protein-protein interaction networks Extensive Tool for Network Analysis (ETNA) allows the topological analysis of protein-protein interaction networks in order to detect bias, e.g., caused by scientific trends. Links to the manuscript and the code repository can be found in the link section. Reference: Alicja W Nowakowska, Malgorzata Kotulska, Topological analysis as a tool for detection of abnormalities in proteinprotein interaction data, Bioinformatics, Volume 38, Issue 16, 15 August 2022, Pages 39683975, https://doi.org/10.1093/bioinformatics/btac440 Natural science - Biology - Computational biology - Network analysis Posted by team TransBio from: Zentralinstitut für Seelische Gesundheit

1

Biological Random Walks for the prioritization of disease genes in the human interactome The Biological Random Walks (BRW) method allows the integrative, network-based analysis of multiple data modalities, in order to prioritize genes with regards to their potential disease relevance. Links to the manuscript and code can be found in the links section. Citation: Michele Gentili, Leonardo Martini, Marialuisa Sponziello, Luca Becchetti, Biological Random Walks: multi-omics integration for disease gene prioritization, Bioinformatics, Volume 38, Issue 17, 1 September 2022, Pages 41454152, https://doi.org/10.1093/bioinformatics/btac446 Natural science - Biology - Computational biology - Network analysis Posted by team TransBio from: Zentralinstitut für Seelische Gesundheit

1

The REALGAR web app of integrated respiratory omics data REALGAR (Reducing Associations by Linking Genes and omics Results) is an integrated omics resource that comprises multi-layered information for gene-centric analysis and visualization of different omics data types in the context of respiratory disease. The links to manuscript, web app and source code can be found in the links section. Citation: Mengyuan Kan, Avantika R Diwadkar, Supriya Saxena, Haoyue Shuai, Jaehyun Joo, Blanca E Himes, REALGAR: a web app of integrated respiratory omics data, Bioinformatics, Volume 38, Issue 18, 15 September 2022, Pages 44424445, https://doi.org/10.1093/bioinformatics/btac524 Applied science - Medicine and health - Respiratory therapy - Data resource Posted by team TransBio from: Zentralinstitut für Seelische Gesundheit

1

The DiNAMIC.Duo R package for the detection of somatic DNA copy number differences without normal reference The DiNAMIC.Duo R package allows the identification of recurrent copy number alterations or their differences in one or more cohorts. The links to the manuscript and the code are shown in the links section. Citation: Vonn Walter, Hyo Young Choi, Xiaobei Zhao, Yan Gao, Jeremiah Holt, D Neil Hayes, DiNAMIC.Duo: detecting somatic DNA copy number differences without a normal reference, Bioinformatics, Volume 38, Issue 18, 15 September 2022, Pages 44154417, https://doi.org/10.1093/bioinformatics/btac542 Natural science - Biology - Genetics - DNA copy number Posted by team TransBio from: Zentralinstitut für Seelische Gesundheit

1

A deep transfer learning approach to identify retinal cell (sub)types from single-cell RNA sequencing data This deep transfer learning approach was designed to identify retinal cell types and retinal ganglion cell subtypes with high accuracy from single-cell RNA sequencing data. Links to the manuscript and method can be found in the links section. Please see reference: Yeganeh Madadi, Jian Sun, Hao Chen, Robert Williams, Siamak Yousefi, Detecting retinal neural and stromal cell classes and ganglion cell subtypes based on transcriptome data with deep transfer learning, Bioinformatics, Volume 38, Issue 18, 15 September 2022, Pages 43214329, https://doi.org/10.1093/bioinformatics/btac514 Natural science - Biology - Genetics - Single-Cell RNA Sequencing Posted by team TransBio from: Zentralinstitut für Seelische Gesundheit

1

DrDimont R package for the prediction of drug response via multi-omics network analyis The DrDimont R package allows the comparative analyses of multiple multi-omics networks and uses these for the inference of drug response that is explainable with regards to the underlying molecular differences. Links to the manuscript and code are provided in the links section. Citation: Pauline Hiort, Julian Hugo, Justus Zeinert, Nataniel Mller, Spoorthi Kashyap, Jagath C Rajapakse, Francisco Azuaje, Bernhard Y Renard, Katharina Baum, DrDimont: explainable drug response prediction from differential analysis of multi-omics networks, Bioinformatics, Volume 38, Issue Supplement_2, September 2022, Pages ii113ii119, https://doi.org/10.1093/bioinformatics/btac477 Natural science - Biology - Bioinformatics - Multi-omics analysis Posted by team TransBio from: Zentralinstitut für Seelische Gesundheit

1

permGWAS - a permutation-based LMM method to compute adjusted significance thresholds in GWAS permGWAS is open-source, publicly available tool for the permutation-based determination of genotype-phenotype relationships while accounting for population structure and cryptic relatedness. Links to the manuscript and GitHub can be found in the links section. Citation: Maura John, Markus J Ankenbrand, Carolin Artmann, Jan A Freudenthal, Arthur Korte, Dominik G Grimm, Efficient permutation-based genome-wide association studies for normal and skewed phenotypic distributions, Bioinformatics, Volume 38, Issue Supplement_2, September 2022, Pages ii5ii12, https://doi.org/10.1093/bioinformatics/btac455 Natural science - Biology - Genetics - GWAS Posted by team TransBio from: Zentralinstitut für Seelische Gesundheit

1

recount3 and snapcount R/Bioconductor packages for investigation of 750,000 publicly available RNAseq samples from mouse and human. The recount3 resource provides access to data from over 750,000 RNAseq samples that were processed in a uniform manner using the Monorail pipeline. Links to the manuscript and pipeline can be found in the links section. Citation: Wilks, C., Zheng, S.C., Chen, F.Y. et al. recount3: summaries and queries for large-scale RNA-seq expression and splicing. Genome Biol 22, 323 (2021). https://doi.org/10.1186/s13059-021-02533-6 Natural science - Biology - Genetics - RNA Sequencing Posted by team TransBio from: Zentralinstitut für Seelische Gesundheit

1

R package SCpubr: Single-Cell trascriptomics in a publication ready format The SCpubr allows creating publication-ready plots for single-cell RNA Sequencing. Links to the SCpubr documentation, the GitHub repository, and the Bioarxiv publication are provided in the links. Please see reference: Blanco-Carmona, E. Generating publication ready visualizations for Single Cell transcriptomics using SCpubr. bioRxiv 2022.02.28.482303; doi: https://doi.org/10.1101/2022.02.28.482303 Natural science - Biology - Genetics - Single-Cell RNA Sequencing Posted by team TransBio from: Zentralinstitut für Seelische Gesundheit

1

Symplur Signals hashtag analytics tool for Twitter hashtag research The Symplur Signals tool allows the tracking of tweets containing hashtags that are pre-registered with the Symplur healthcare hashtag project [1]. The method allows the extraction of quantitative properties, such as the cumulative number of tweets, impressions, and unique users sharing tweets containing a given hashtag. The method is described in [2], for a link to the manuscript, see link above. [1] Symplur . The Healthcare Hashtag Project - #MedTwitterAI. (2021). Available online at: https://www.symplur.com/healthcare-hashtags [2] Nawaz FA, Barr AA, Desai MY, Tsagkaris C, Singh R, Klager E, Eibensteiner F, Parvanov ED, Hribersek M, Kletecka-Pulker M, Willschke H, Atanasov AG. Promoting Research, Awareness, and Discussion on AI in Medicine Using #MedTwitterAI: A Longitudinal Twitter Hashtag Analysis. Front Public Health. 2022 Jul 1;10:856571. doi: 10.3389/fpubh.2022.856571. Applied science - Medicine and health - Science communication in medicine - Social media Posted by team TransBio from: Zentralinstitut für Seelische Gesundheit

1