Using large-scale perturbations in gene network reconstruction

MacCarthy, Thomas; Pomiankowski, Andrew; Seymour, Robert M.

doi:10.1186/1471-2105-6-11

Cited by 21 publications

(5 citation statements)

References 19 publications

(27 reference statements)

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“…The BioCreative challenges are an ongoing effort to promote the evaluation and development of text mining and natural language processing software for the life sciences community [ 37 ]. Carrying out this task within the organization of BioCreative was especially useful due to the previous experiences of this community with related bio-medical NER tasks (the Gene Mention recognition tasks of BioCreative I and II [ 38 , 39 ], as well as the Gene Normalization tasks [ 40 ]).…”

Section: Introductionmentioning

confidence: 99%

The CHEMDNER corpus of chemicals and drugs and its annotation principles

et al. 2015

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The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/

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Section: Introductionmentioning

confidence: 99%

The CHEMDNER corpus of chemicals and drugs and its annotation principles

et al. 2015

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“…In particular, many TFs are grouped in families that have highly similar consensus sequences [32]. For these, we will interpret data for a given consensus sequence as being indicative of higher levels of protein for the family of TFs.…”

Section: Resultsmentioning

confidence: 99%

Quantitative, solution-phase profiling of multiple transcription factors in parallel

et al. 2013

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Transcription factors are regulatory proteins that bind to specific sites of chromosomal DNA to enact responses to intracellular and extracellular stimuli. Transcription factor signalling networks are branched and interconnected so that any single transcription factor can activate many different genes and one gene can be activated by a combination of different transcription factors. Thus, trying to characterize a cellular response to a stimulus by measuring the level of only one transcription factor potentially ignores important simultaneous events that contribute to the response. Hence, parallel measurements of transcription factors are necessary to capture the breadth of valuable information about cellular responses that would not be obtained by measuring only a single transcription factor. We have sought to develop a new, scalable, flexible, and sensitive approach to analysis of transcription factor levels that complements existing parallel approaches. Here, we describe proof-of-principle analyses of purified human transcription factors and breast cancer nuclear extracts. Our assay can successfully quantify transcription factors in parallel with ~10-fold better sensitivity than current techniques. Sensitivity of the assay can be further increased by 200-fold through the use of PCR for signal amplification.

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“…All of them can be directly applied to gene perturbation screens, even though some of them have been introduced in different contexts. While this review has focused on single gene knock-outs and knock-downs, similar approaches can be applied to gene over-expression screens [22] , [83] , [93] , [94] , drug treatment [84] , environmental stresses changing many genes [95] , [96] , or even natural genetic variation [97] .…”

Section: Discussion and Outlookmentioning

confidence: 99%

How to Understand the Cell by Breaking It: Network Analysis of Gene Perturbation Screens

Markowetz

2010

PLoS Comput Biol

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Modern high-throughput gene perturbation screens are key technologies at the forefront of genetic research. Combined with rich phenotypic descriptors they enable researchers to observe detailed cellular reactions to experimental perturbations on a genome-wide scale. This review surveys the current stateof-the-art in analyzing single gene perturbation screens from a network point of view. We describe approaches to make the step from the parts list to the wiring diagram by using phenotypes for network inference and integrating them with complementary data sources. The first part of the review describes methods to analyze one-or low-dimensional phenotypes like viability or reporter activity; the second part concentrates on high-dimensional phenotypes showing global changes in cell morphology, transcriptome or proteome.

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Using large-scale perturbations in gene network reconstruction

Cited by 21 publications

References 19 publications

The CHEMDNER corpus of chemicals and drugs and its annotation principles

The CHEMDNER corpus of chemicals and drugs and its annotation principles

Quantitative, solution-phase profiling of multiple transcription factors in parallel

How to Understand the Cell by Breaking It: Network Analysis of Gene Perturbation Screens

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