Development of a Comprehensive Sequencing Assay for Inherited Cardiac Condition Genes.

Inherited cardiac conditions (ICCs) are characterised by marked genetic and allelic heterogeneity and require extensive sequencing for genetic characterisation. We iteratively optimised a targeted gene capture panel for ICCs that includes disease-causing, putatively pathogenic, research and phenocopy genes (n = 174 genes). We achieved high coverage of the target region on both MiSeq (>99.8% at ≥ 20× read depth, n = 12) and NextSeq (>99.9% at ≥ 20×, n = 48) platforms with 100% sensitivity and precision for single nucleotide variants and indels across the protein-coding target on the MiSeq. In the final assay, 40 out of 43 established ICC genes informative in clinical practice achieved complete coverage (100 % at ≥ 20×). By comparison, whole exome sequencing (WES; ∼ 80×), deep WES (∼ 500×) and whole genome sequencing (WGS; ∼ 70×) had poorer performance (88.1, 99.2 and 99.3% respectively at ≥ 20×) across the ICC target. The assay described here delivers highly accurate and affordable sequencing of ICC genes, complemented by accessible cloud-based computation and informatics. See Editorial in this issue (DOI: 10.1007/s12265-015-9667-8 ).

Alternative Splicing Signatures in RNA-seq Data: Percent Spliced in (PSI).

Thousands of alternative exons are spliced out of messenger RNA to increase protein diversity. High-throughput sequencing of short cDNA fragments (RNA-seq) generates a genome-wide snapshot of these post-transcriptional processes. RNA-seq reads yield insights into the regulation of alternative splicing by revealing the usage of known or unknown splice sites as well as the expression level of exons. Constitutive exons are never covered by split alignments, whereas alternative exonic parts are located within highly expressed splicing junctions. The ratio between reads including or excluding exons, also known as percent spliced in index (PSI), indicates how efficiently sequences of interest are spliced into transcripts. This protocol describes a method to calculate the PSI without prior knowledge of splicing patterns. It provides a quantitative, global assessment of exon usage that can be integrated with other tools that identify differential isoform processing. Novel, complex splicing events along a genetic locus can be visualized in an exon-centric manner and compared across conditions.

Extracting rate changes in transcriptional regulation from MEDLINE abstracts.

BACKGROUND: Time delays are important factors that are often neglected in gene regulatory network (GRN) inference models. Validating time delays from knowledge bases is a challenge since the vast majority of biological databases do not record temporal information of gene regulations. Biological knowledge and facts on gene regulations are typically extracted from bio-literature with specialized methods that depend on the regulation task. In this paper, we mine evidences for time delays related to the transcriptional regulation of yeast from the PubMed abstracts. RESULTS: Since the vast majority of abstracts lack quantitative time information, we can only collect qualitative evidences of time delays. Specifically, the speed-up or delay in transcriptional regulation rate can provide evidences for time delays (shorter or longer) in GRN. Thus, we focus on deriving events related to rate changes in transcriptional regulation. A corpus of yeast regulation related abstracts was manually labeled with such events. In order to capture these events automatically, we create an ontology of sub-processes that are likely to result in transcription rate changes by combining textual patterns and biological knowledge. We also propose effective feature extraction methods based on the created ontology to identify the direct evidences with specific details of these events. Our ontologies outperform existing state-of-the-art gene regulation ontologies in the automatic rule learning method applied to our corpus. The proposed deterministic ontology rule-based method can achieve comparable performance to the automatic rule learning method based on decision trees. This demonstrates the effectiveness of our ontology in identifying rate-changing events. We also tested the effectiveness of the proposed feature mining methods on detecting direct evidence of events. Experimental results show that the machine learning method on these features achieves an F1-score of 71.43%. CONCLUSIONS: The manually labeled corpus of events relating to rate changes in transcriptional regulation for yeast is available in https://sites.google.com/site/wentingntu/data. The created ontologies summarized both biological causes of rate changes in transcriptional regulation and corresponding positive and negative textual patterns from the corpus. They are demonstrated to be effective in identifying rate-changing events, which shows the benefits of combining textual patterns and biological knowledge on extracting complex biological events.