Simple SVM based whole-genome segmentation

We present a support vector machine (SVM) based framework for DNA segmentation into binary classes. Two applications are explored: transcription start site prediction and transcription factor binding prediction. Experiments demonstrate our approach has significantly better performance than other methods on both tasks

Sequencing Structural Variants in Cancer for Precision Therapeutics.

The identification of mutations that guide therapy selection for patients with cancer is now routine in many clinical centres. The majority of assays used for solid tumour profiling use DNA sequencing to interrogate somatic point mutations because they are relatively easy to identify and interpret. Many cancers, however, including high-grade serous ovarian, oesophageal, and small-cell lung cancer, are driven by somatic structural variants that are not measured by these assays. Therefore, there is currently an unmet need for clinical assays that can cheaply and rapidly profile structural variants in solid tumours. In this review we survey the landscape of 'actionable' structural variants in cancer and identify promising detection strategies based on massively-parallel sequencing.This work was supported by Cancer Research UK [grant numbers A15973, A15601: 454 G.M, J.D.B], VUmc Cancer Center Amsterdam [VUmc-CCA: BY] and the Dutch 455 Cancer Society [VU 2015-7882: BY].This is the author accepted manuscript. The final version is available from Cell/Elsevier via http://dx.doi.org/10.1016/j.tig.2016.07.00

Heart Rate Variability as a Predictor of Speaking Anxiety

This study examines the relations among the perception of speaking anxiety and difficulties in emotion regulation with 2 measures of physiological activity: heart rate (HR) and heart rate variability (HRV). Results show significant changes in HR and state anxiety, but not HRV, among the 6 experimental conditions: quiet, reading in both sitting and standing positions, and speaking in both sitting and standing positions. HRV significantly and negatively correlated with difficulties in emotion regulation and HR, but not with public speaking apprehension (PSA) scores or state anxiety ratings. PSA scores, however, were significantly and positively correlated with state anxiety ratings. Results are interpreted in terms of the simultaneous, coordinated operation of physical reactions and emotional coping strategies

Paving the Way Towards a Successful and Fulfilling Career in Computational Biology

Most of us will spend a significant amount of time and effort throughout our lives in improving our career. The decisions we make shape how our career progresses, and the right decisions can ensure it is successful and fulfilling. Early decisions can have a strong influence, especially in today’s competitive job market, where a university degree will not guarantee the best job. It is vital these early decisions are well informed and based on access to as much information as possible. As part of an effort to ensure that computational biologists and students are guided into the right career paths, the Regional Student Group (RSG) program, an arm of the International Society for Computational Biology (ISCB), has provided a range of activities to assist computational biologists and bioinformatics researchers in their career development. These include organizing prac�tical workshops and seminars presented by leading experts on how to broaden the scope of career options and guarantee success. This article provides insight on some of these activities and highlights the benefits gained through the shared experiences of RSGs in running career-related activities

A bi-ordering approach to linking gene expression with clinical annotations in gastric cancer

<p>Abstract</p> <p>Background</p> <p>In the study of cancer genomics, gene expression microarrays, which measure thousands of genes in a single assay, provide abundant information for the investigation of interesting genes or biological pathways. However, in order to analyze the large number of noisy measurements in microarrays, effective and efficient bioinformatics techniques are needed to identify the associations between genes and relevant phenotypes. Moreover, systematic tests are needed to validate the statistical and biological significance of those discoveries.</p> <p>Results</p> <p>In this paper, we develop a robust and efficient method for exploratory analysis of microarray data, which produces a number of different orderings (rankings) of both genes and samples (reflecting correlation among those genes and samples). The core algorithm is closely related to biclustering, and so we first compare its performance with several existing biclustering algorithms on two real datasets - gastric cancer and lymphoma datasets. We then show on the gastric cancer data that the sample orderings generated by our method are highly statistically significant with respect to the histological classification of samples by using the Jonckheere trend test, while the gene modules are biologically significant with respect to biological processes (from the Gene Ontology). In particular, some of the gene modules associated with biclusters are closely linked to gastric cancer tumorigenesis reported in previous literature, while others are potentially novel discoveries.</p> <p>Conclusion</p> <p>In conclusion, we have developed an effective and efficient method, Bi-Ordering Analysis, to detect informative patterns in gene expression microarrays by ranking genes and samples. In addition, a number of evaluation metrics were applied to assess both the statistical and biological significance of the resulting bi-orderings. The methodology was validated on gastric cancer and lymphoma datasets.</p

Inferring structural variant cancer cell fraction.

We present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone's performance is comparable to single-nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy-number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity

Highlights from the Student Council Symposium 2011 at the International Conference on Intelligent Systems for Molecular Biology and European Conference on Computational Biology

The Student Council (SC) of the International Society for Computational Biology (ISCB) organized their annual symposium in conjunction with the Intelligent Systems for Molecular Biology (ISMB) conference