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BEDTools: a flexible suite of utilities for comparing genomic features.

ABSTRACT: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner.This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets.BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtoolsaaronquinlan@gmail.com; imh4y@virginia.eduSupplementary data are available at Bioinformatics online.

SUBMITTER: Quinlan AR

PROVIDER: S-EPMC2832824 | biostudies-other | 2010 Mar

REPOSITORIES: biostudies-other

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BEDTools: a flexible suite of utilities for comparing genomic features.

Quinlan Aaron R AR Hall Ira M IM

Bioinformatics (Oxford, England) 20100128 6

<h4>Motivation</h4>Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner.<h4>Results</h4>This article introduces a new software suite for the compa ...[more]

PMID: 20110278

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Project description:BackgroundCis-regulatory elements (CREs) are DNA sequence segments that regulate gene expression. Among CREs are promoters, enhancers, Boundary Elements (BEs) and Polycomb Response Elements (PREs), all of which are enriched in specific sequence motifs that form particular occurrence landscapes. We have recently introduced a hierarchical machine learning approach (SVM-MOCCA) in which Support Vector Machines (SVMs) are applied on the level of individual motif occurrences, modelling local sequence composition, and then combined for the prediction of whole regulatory elements. We used SVM-MOCCA to predict PREs in Drosophila and found that it was superior to other methods. However, we did not publish a polished implementation of SVM-MOCCA, which can be useful for other researchers, and we only tested SVM-MOCCA with IUPAC motifs and PREs.ResultsWe here present an expanded suite for modelling CRE sequences in terms of motif occurrence combinatorics-Motif Occurrence Combinatorics Classification Algorithms (MOCCA). MOCCA contains efficient implementations of several modelling methods, including SVM-MOCCA, and a new method, RF-MOCCA, a Random Forest-derivative of SVM-MOCCA. We used SVM-MOCCA and RF-MOCCA to model Drosophila PREs and BEs in cross-validation experiments, making this the first study to model PREs with Random Forests and the first study that applies the hierarchical MOCCA approach to the prediction of BEs. Both models significantly improve generalization to PREs and boundary elements beyond that of previous methods-including 4-spectrum and motif occurrence frequency Support Vector Machines and Random Forests-, with RF-MOCCA yielding the best results.ConclusionMOCCA is a flexible and powerful suite of tools for the motif-based modelling of CRE sequences in terms of motif composition. MOCCA can be applied to any new CRE modelling problems where motifs have been identified. MOCCA supports IUPAC and Position Weight Matrix (PWM) motifs. For ease of use, MOCCA implements generation of negative training data, and additionally a mode that requires only that the user specifies positives, motifs and a genome. MOCCA is licensed under the MIT license and is available on Github at https://github.com/bjornbredesen/MOCCA .

Project description:BackgroundSimulation-based training allows trainees to experiment during training and end-of-training tests could increase motivation and retention. The aim of this trial was to determine if a simulation-based training program including directed self-regulated learning and post-testing improved clinical outcomes compared to a traditional simulation-based training program.MethodsA randomized trial was conducted involving 32 participants without prior experience in endoscopic procedures. The intervention group practiced independently in a simulation centre and got a post-test whereas the control group received traditional instructions and demonstrations before being allowed to practice. Three weeks after the intervention the participants performed cystoscopies on two consecutive patients. Clinical performance was assessed using a global rating scale (GRS) with established evidence of validity. Independent samples t-test, Cronbach's α, Pearson's r, and paired samples t-test were used for statistical analysis.ResultsTwenty-five participants performed two cystoscopies on patients. There was no significant difference between the two study groups with respect to mean GRS of performance (p = 0.63, 95 % CI; -2.4-3.9). The internal consistency of the global rating scale was high, Cronbach's α = 0.91. Participants from both study groups demonstrated significant improvement between the first and second clinical procedures (p = 0.004, 95 % CI, 0.8-3.5). Eight (32%) and 15 (60%) participants demonstrated adequate clinical skills in their first and second procedure, respectively.ConclusionsNo significant differences were found on the clinical transfer when comparing the two programs. Neither of our training programs was able to ensure consistent, competent performance on patients and this finding could serve as an important argument for simulation-based mastery learning where all training continues until a pre-defined level of proficiency is met.Trial registrationsThe trial was submitted before enrolment of participants to the Regional Scientific Ethics Committee of the Capital Region which established that ethical approval was not necessary (H-4-2014-122). The trial was registered at Clinicaltrials.gov (NCT02411747).

Dataset Information

BEDTools: a flexible suite of utilities for comparing genomic features.

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BEDTools: a flexible suite of utilities for comparing genomic features.

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