ORCID Profile
0000-0001-6910-7306
Current Organisations
AI Digital / Biomatics
,
PA (Pacific Analytics)
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Publisher: Oxford University Press (OUP)
Date: 19-06-2018
DOI: 10.1093/BIOINFORMATICS/BTY492
Abstract: Standardized interfaces for efficiently accessing high-throughput sequencing data are a fundamental requirement for large-scale genomic data sharing. We have developed htsget, a protocol for secure, efficient and reliable access to sequencing read and variation data. We demonstrate four independent client and server implementations, and the results of a comprehensive interoperability demonstration. samtools.github.io/hts-specs/htsget.html Supplementary data are available at Bioinformatics online.
Publisher: IEEE
Date: 11-2011
DOI: 10.1109/BIBM.2011.48
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11893257_16
Publisher: Springer Science and Business Media LLC
Date: 06-2021
DOI: 10.1038/S41431-021-00852-7
Abstract: Reanalysis of inconclusive exome/genome sequencing data increases the diagnosis yield of patients with rare diseases. However, the cost and efforts required for reanalysis prevent its routine implementation in research and clinical environments. The Solve-RD project aims to reveal the molecular causes underlying undiagnosed rare diseases. One of the goals is to implement innovative approaches to reanalyse the exomes and genomes from thousands of well-studied undiagnosed cases. The raw genomic data is submitted to Solve-RD through the RD-Connect Genome-Phenome Analysis Platform (GPAP) together with standardised phenotypic and pedigree data. We have developed a programmatic workflow to reanalyse genome-phenome data. It uses the RD-Connect GPAP’s Application Programming Interface (API) and relies on the big-data technologies upon which the system is built. We have applied the workflow to prioritise rare known pathogenic variants from 4411 undiagnosed cases. The queries returned an average of 1.45 variants per case, which first were evaluated in bulk by a panel of disease experts and afterwards specifically by the submitter of each case. A total of 120 index cases (21.2% of prioritised cases, 2.7% of all exome/genome-negative s les) have already been solved, with others being under investigation. The implementation of solutions as the one described here provide the technical framework to enable periodic case-level data re-evaluation in clinical settings, as recommended by the American College of Medical Genetics.
Publisher: Springer Science and Business Media LLC
Date: 06-2021
DOI: 10.1038/S41431-021-00859-0
Abstract: For the first time in Europe hundreds of rare disease (RD) experts team up to actively share and jointly analyse existing patient’s data. Solve-RD is a Horizon 2020-supported EU flagship project bringing together clinicians, scientists, and patient representatives of 51 sites from 15 countries. Solve-RD is built upon a core group of four European Reference Networks (ERNs ERN-ITHACA, ERN-RND, ERN-Euro NMD, ERN-GENTURIS) which annually see more than 270,000 RD patients with respective pathologies. The main ambition is to solve unsolved rare diseases for which a molecular cause is not yet known. This is achieved through an innovative clinical research environment that introduces novel ways to organise expertise and data. Two major approaches are being pursued (i) massive data re-analysis of ,000 unsolved rare disease patients and (ii) novel combined -omics approaches. The minimum requirement to be eligible for the analysis activities is an inconclusive exome that can be shared with controlled access. The first preliminary data re-analysis has already diagnosed 255 cases form 8393 exomes/genome datasets. This unprecedented degree of collaboration focused on sharing of data and expertise shall identify many new disease genes and enable diagnosis of many so far undiagnosed patients from all over Europe.
Publisher: Springer Science and Business Media LLC
Date: 10-05-2021
Publisher: Springer Science and Business Media LLC
Date: 26-06-2015
DOI: 10.1038/NG.3312
Publisher: IEEE
Date: 12-2007
Publisher: Elsevier BV
Date: 02-2023
Publisher: Oxford University Press (OUP)
Date: 05-02-2021
DOI: 10.1093/BIOINFORMATICS/BTAB087
Abstract: The majority of genome analysis tools and pipelines require data to be decrypted for access. This potentially leaves sensitive genetic data exposed, either because the unencrypted data is not removed after analysis, or because the data leaves traces on the permanent storage medium. : We defined a file container specification enabling direct byte-level compatible random access to encrypted genetic data stored in community standards such as SAM/BAM/CRAM/VCF/BCF. By standardizing this format, we show how it can be added as a native file format to genomic libraries, enabling direct analysis of encrypted data without the need to create a decrypted copy. The Crypt4GH specification can be found at: samtools.github.io/hts-specs/crypt4gh.pdf. Supplementary data are available at Bioinformatics online.
Publisher: Elsevier BV
Date: 11-2021
Publisher: Oxford University Press (OUP)
Date: 31-08-2009
DOI: 10.1093/BIOINFORMATICS/BTP521
Abstract: Motivation: The sequencing of whole genomes from various species has provided us with a wealth of genetic information. To make use of the vast amounts of data available today it is necessary to devise computer-based analysis techniques. Results: We propose a Hidden Markov Model (HMM) based algorithm to detect groups of genes functionally similar to a set of input genes from microarray expression data. A subset of experiments from a microarray is selected based on a set of related input genes. HMMs are trained from the input genes and a group of random gene input sets to provide significance estimates. Every gene in the microarray is scored using all HMMs and significant matches with the input genes are retained. We ran this algorithm on the life cycle of Drosophila microarray data set with KEGG pathways for cell cycle and translation factors as input data sets. Results show high functional similarity in resulting gene sets, increasing our biological insight into gene pathways and KEGG annotations. The algorithm performed very well compared to the Signature Algorithm and a purely correlation-based approach. Availability: Java source codes and data sets are available at www.ittc.ku.edu/∼xwchen/software.htm Contact: xwchen@ittc.ku.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Alexander Senf.