Person:

Liu, Jun

In the past decade computational biology has grown from a cottage industry with a handful of researchers to an attractive interdisciplinary field, catching the attention and imagination of many quantitatively-minded scientists. Of interest to us is the key role played by the EM algorithm during this transformation. We survey the use of the EM algorithm in a few important computational biology problems surrounding the “central dogma” of molecular biology: from DNA to RNA and then to proteins. Topics of this article include sequence motif discovery, protein sequence alignment, population genetics, evolutionary models and mRNA expression microarray data analysis.

Responses of 42 people with aphasia to 11 sentence types in enactment and sentence–picture matching tasks were characterized using Rasch models that varied in the inclusion of the factors of task, sentence type, and patient group. The best fitting models required the factors of task and patient group but not sentence type. The results provide evidence that aphasic syntactic comprehension is best accounted for by models that include different estimates of patient ability in different tasks and different difficulty of all sentences in different groups of patients, but that do not include different estimates of patient ability for different types of sentences.

A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological datasets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here, we performed a genome-wide association study (GWAS) meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ~10 million single nucleotide polymorphisms (SNPs). We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 1012–4. We devised an in-silico pipeline using established bioinformatics methods based on functional annotation5, cis-acting expression quantitative trait loci (cis-eQTL)6, and pathway analyses7–9 – as well as novel methods based on genetic overlap with human primary immunodeficiency (PID), hematological cancer somatic mutations and knock-out mouse phenotypes – to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.

Current disease association studies are routinely conducted on a genome-wide scale, testing hundreds of thousands or millions of genetic markers. Besides detecting marginal associations of individual markers with the disease, it is also of interest to identify gene–gene and gene–environment interactions, which confer susceptibility to the disease risk. The astronomical number of possible combinations of markers and environmental factors, however, makes interaction mapping a daunting task both computationally and statistically. In this paper, we review and discuss a set of Bayesian partition methods developed recently for mapping single-nucleotide polymorphisms in case-control studies, their extension to quantitative traits, and further generalization to multiple traits. We use simulation and real data sets to demonstrate the performance of these methods, and we compare them with some existing interaction mapping algorithms. With the recent advance in high-throughput sequencing technologies, genome-wide measurements of epigenetic factor enrichment, structural variations, and transcription activities become available at the individual level. The tsunami of data creates more challenges for gene–gene interaction mapping, but at the same time provides new opportunities that, if utilized properly through sophisticated statistical means, can improve the power of mapping interactions at the genome scale.

The maternal and paternal genomes play different roles in mammalian brains as a result of genomic imprinting, an epigenetic regulation leading to differential expression of the parental alleles of some genes. Here we investigate genomic imprinting in the cerebellum using a newly developed Bayesian statistical model that provides unprecedented transcript-level resolution. We uncover 160 imprinted transcripts, including 41 novel and independently validated imprinted genes. Strikingly, many genes exhibit parentally biased—rather than monoallelic—expression, with different magnitudes according to age, organ, and brain region. Developmental changes in parental bias and overall gene expression are strongly correlated, suggesting combined roles in regulating gene dosage. Finally, brain-specific deletion of the paternal, but not maternal, allele of the paternally-biased Bcl-x, (Bcl2l1) results in loss of specific neuron types, supporting the functional significance of parental biases. These findings reveal the remarkable complexity of genomic imprinting, with important implications for understanding the normal and diseased brain. DOI: http://dx.doi.org/10.7554/eLife.07860.001

BACKGROUND: Methods for near-real-time monitoring of new drugs in electronic healthcare data are needed. OBJECTIVE: In a novel application, we prospectively monitored ischemic, bleeding, and mortality outcomes among patients initiating prasugrel versus clopidogrel in routine care during the first 2 years following the approval of prasugrel. METHODS: Using the HealthCore Integrated Research Database, we conducted a prospective cohort study comparing prasugrel and clopidogrel initiators in the 6 months following the introduction of prasugrel and every 2 months thereafter. We identified patients who initiated antiplatelets within 14 days following discharge from hospitalizations for myocardial infarction (MI) or acute coronary syndrome. We matched patients using high-dimensional propensity scores (hd-PSs) and followed them for ischemic (i.e., MI and ischemic stroke) events, bleed (i.e., hemorrhagic stroke and gastrointestinal bleed) events, and all-cause mortality. For each outcome, we applied sequential alerting algorithms. RESULTS: We identified 1,282 eligible new users of prasugrel and 8,263 eligible new users of clopidogrel between September 2009 and August 2011. In hd-PS matched cohorts, the overall MI rate difference (RD) comparing prasugrel with clopidogrel was -23.1 (95 % confidence interval [CI] -62.8-16.7) events per 1,000 person-years and RDs were -0.5 (-12.9-11.9) and -2.8 (-13.2-7.6) for a composite bleed event outcome and death from any cause, respectively. No algorithms generated alerts for any outcomes. CONCLUSIONS: Near-real-time monitoring was feasible and, in contrast to the key pre-marketing trial that demonstrated the efficacy of prasugrel, did not suggest that prasugrel compared with clopidogrel was associated with an increased risk of gastrointestinal and intracranial bleeding.

BACKGROUND: Many patients with type 2 diabetes eventually require insulin, yet little is known about the patterns and quality of pharmacologic care received following insulin initiation. Guidelines from the American Diabetes Association and the European Association for the Study of Diabetes recommend that insulin secretagogues such as sulfonylureas be discontinued at the time of insulin initiation to reduce the risk of hypoglycemia, and that treatment be intensified if HbA1c levels remain above-target 3 months after insulin initiation. OBJECTIVE: To describe pharmacologic treatment patterns over time among adults initiating insulin and/or intensifying insulin treatment. DESIGN: Observational study. SUBJECTS: A large commercially insured population of adult patients without recorded type 1 diabetes who initiated insulin. MAIN MEASURES: We evaluated changes in non-insulin antidiabetic medication use during the 120 days immediately following insulin initiation, rates of increase in insulin dose and/or dosing frequency during the 270 days following an insulin initiation treatment period of 90 days, and rates of insulin discontinuation. KEY RESULTS: Seven thousand, nine hundred and thirty-two patients initiated insulin during 2003-2008, with the majority (61 %) initiating basal insulin only. Metformin (55 %), sulfonylureas (39 %), and thiazolidinediones (30 %) were commonly used prior to insulin initiation. Metformin was continued by 64 % of patients following mixed or mealtime insulin initiation; the continuation rate was nearly as high for sulfonylureas (58 %). Insulin dose and/or dosing frequency increased among 22.9 % of patients. Insulin was discontinued by 27 % of patients. CONCLUSIONS: We found evidence of substantial departures from guideline-recommended pharmacotherapy. Insulin secretagogues were frequently co-prescribed with insulin. The majority of patients had no evidence of treatment intensification following insulin initiation, although this finding is difficult to interpret without HbA1c levels. While each patient's care should be individualized, our data suggest that the quality of care following insulin initiation can be improved.

A model system for investigating how developmental regulatory networks determine cell fate is spore formation in Bacillus subtilis. The master regulator for sporulation is Spo0A, which is activated by phosphorylation via a phosphorelay that is subject to three positive feedback loops. The ultimate decision to sporulate is, however, stochastic in that only a portion of the population sporulates even under optimal conditions. It was previously assumed that activation of Spo0A and hence entry into sporulation is subject to a bistable switch mediated by one or more feedback loops. Here we reinvestigate the basis for bimodality in sporulation. We show that none of the feedback loops is rate limiting for the synthesis and phosphorylation of Spo0A. Instead, the loops ensure a just-in-time supply of relay components for rising levels of phosphorylated Spo0A, with phosphate flux through the relay being limiting for Spo0A activation and sporulation. In addition, genes under Spo0A control did not exhibit a bimodal pattern of expression as expected for a bistable switch. In contrast, we observed a highly heterogeneous pattern of Spo0A activation that increased in a nonlinear manner with time. We present a computational model for the nonlinear increase and propose that the phosphorelay is a noise generator and that only cells that attain a threshold level of phosphorylated Spo0A sporulate.

Motif discovery is an important topic in computational transcriptional regulation studies. In the past decade, many researchers have contributed to the field and many de novo motif-finding tools have been developed, each may have a different strength. However, most of these tools do not have a user-friendly interface and their results are not easily comparable. We present a software called Toolbox of Motif Discovery (Tmod) for Windows operating systems. The current version of Tmod integrates 12 widely used motif discovery programs: MDscan, BioProspector, AlignACE, Gibbs Motif Sampler, MEME, CONSENSUS, MotifRegressor, GLAM, MotifSampler, SeSiMCMC, Weeder and YMF. Tmod provides a unified interface to ease the use of these programs and help users to understand the tuning parameters. It allows plug-in motif-finding programs to run either separately or in a batch mode with predetermined parameters, and provides a summary comprising of outputs from multiple programs. Tmod is developed in C++ with the support of Microsoft Foundation Classes and Cygwin. Tmod can also be easily expanded to include future algorithms.

The effort to identify genes with periodic expression during the cell cycle from genome-wide microarray time series data has been ongoing for a decade. However, the lack of rigorous modeling of periodic expression as well as the lack of a comprehensive model for integrating information across genes and experiments has impaired the effort for the accurate identification of periodically expressed genes. To address the problem, we introduce a Bayesian model to integrate multiple independent microarray data sets from three recent genome-wide cell cycle studies on fission yeast. A hierarchical model was used for data integration. In order to facilitate an efficient Monte Carlo sampling from the joint posterior distribution, we develop a novel Metropolis–Hastings group move. A surprising finding from our integrated analysis is that more than 40% of the genes in fission yeast are significantly periodically expressed, greatly enhancing the reported 10–15% of the genes in the current literature. It calls for a reconsideration of the periodically expressed gene detection problem.