Towards Better Understanding of Artifacts in Variant Calling from High-Coverage Samples

Heng Li
(Submitted on 3 Apr 2014)

Motivation: Whole-genome high-coverage sequencing has been widely used for personal and cancer genomics as well as in various research areas. However, in the lack of an unbiased whole-genome truth set, the global error rate of variant calls and the leading causal artifacts still remain unclear even given the great efforts in the evaluation of variant calling methods.
Results: We made ten SNP and INDEL call sets with two read mappers and five variant callers, both on a haploid human genome and a diploid genome at a similar coverage. By investigating false heterozygous calls in the haploid genome, we identified the erroneous realignment in low-complexity regions and the incomplete reference genome with respect to the sample as the two major sources of errors, which press for continued improvements in these two areas. We estimated that the error rate of raw genotype calls is as high as 1 in 10-15kb, but the error rate of post-filtered calls is reduced to 1 in 100-200kb without significant compromise on the sensitivity.

Comparison of the theoretical and real-world evolutionary potential of a genetic circuit.

Manuel Razo-Mejia, James Boedicker, Daniel Jones, Alexander de Luna, Justin Block Kinney, Rob Phillips

With the development of next-generation sequencing technologies, many large scale experimental efforts aim to map genotypic variability among individuals. This natural variability in populations fuels many fundamental biological processes, ranging from evolutionary adaptation and speciation to the spread of genetic diseases and drug resistance. An interesting and important component of this variability is present within the regulatory regions of genes. As these regions evolve, accumulated mutations lead to modulation of gene expression, which may have consequences for the phenotype. A simple model system where the link between genetic variability, gene regulation and function can be studied in detail is missing. In this article we develop a model to explore how the sequence of the wild-type lac promoter dictates the fold change in gene expression. The model combines single-base pair resolution maps of transcription factor and RNA polymerase binding energies with a comprehensive thermodynamic model of gene regulation. The model was validated by predicting and then measuring the variability of lac operon regulation in a collection of natural isolates. We then implement the model to analyze the sensitivity of the promoter sequence to the regulatory output, and predict the potential for regulation to evolve due to point mutations in the promoter region.

Flexible isoform-level differential expression analysis with Ballgown

Alyssa C Frazee, Geo Pertea, Andrew E Jaffe, Ben Langmead, Steven L Salzberg, Jeffrey T Leek

We have built a statistical package called Ballgown for estimating differential expression of genes, transcripts, or exons from RNA sequencing experiments. Ballgown is designed to work with the popular Cufflinks transcript assembly software and uses well-motivated statistical methods to provide estimates of changes in expression. It permits statistical analysis at the transcript level for a wide variety of experimental designs, allows adjustment for confounders, and handles studies with continuous covariates. Ballgown provides improved statistical significance estimates as compared to the Cuffdiff differential expression tool included with Cufflinks. We demonstrate the flexibility of the Ballgown package by re-analyzing 667 samples from the GEUVADIS study to identify transcript-level eQTLs and identify non-linear artifacts in transcript data. Our package is freely available from: https://github.com/alyssafrazee/ballgown

Predicting discovery rates of genomic features

Simon Gravel, NHLBI GO Exome Sequencing Project
(Submitted on 13 Mar 2014)

Successful sequencing experiments require judicious sample selection. However, this selection must often be performed on the basis of limited preliminary data. Predicting the statistical properties of the final sample based on preliminary data can be challenging, because numerous uncertain model assumptions may be involved. Here, we ask whether we can predict “omics” variation across many samples by sequencing only a fraction of them. In the infinite-genome limit, we find that a pilot study sequencing 5% of a population is sufficient to predict the number of genetic variants in the entire population within 6% of the correct value, using an estimator agnostic to demography, selection, or population structure. To reach similar accuracy in a finite genome with millions of polymorphisms, the pilot study would require about 15% of the population. We present computationally efficient jackknife and linear programming methods that exhibit substantially less bias than the state of the art when applied to simulated data and sub-sampled 1000 Genomes Project data. Extrapolating based on the NHLBI Exome Sequencing Project data, we predict that 7.2% of sites in the capture region would be variable in a sample of 50,000 African-Americans, and 8.8% in a European sample of equal size. Finally, we show how the linear programming method can also predict discovery rates of various genomic features, such as the number of transcription factor binding sites across different cell types.

Alignathon: A competitive assessment of whole genome alignment methods.

Dent Earl, Ngan K Nguyen, Glenn Hickey, Robert S. Harris, Stephen Fitzgerald, Kathryn Beal, Igor Seledtsov, Vladimir Molodtsov, Brian Raney, Hiram Clawson, Jaebum Kim, Carsten Kemena, Jia-Ming Chang, Ionas Erb, Alexander Poliakov, Minmei Hou, Javier Herrero, Victor Solovyev, Aaron E. Darling, Jian Ma, Cedric Notredame, Michael Brudno, Inna Dubchak, David Haussler, Benedict Paten

Background: Multiple sequence alignments (MSAs) are a prerequisite for a wide variety of evolutionary analyses. Published assessments and benchmark datasets for protein and, to a lesser extent, global nucleotide MSAs are available, but less effort has been made to establish benchmarks in the more general problem of whole genome alignment (WGA). Results: Using the same model as the successful Assemblathon competitions, we organized a competitive evaluation in which teams submitted their alignments, and assessments were performed collectively after all the submissions were received. Three datasets were used: two of simulated primate and mammalian phylogenies, and one of 20 real fly genomes. In total 35 submissions were assessed, submitted by ten teams using 12 different alignment pipelines. Conclusions: We found agreement between independent simulation-based and statistical assessments, indicating that there are substantial accuracy differences between contemporary alignment tools. We saw considerable difference in the alignment quality of differently annotated regions, and found few tools aligned the duplications analysed. We found many tools worked well at shorter evolutionary distances, but fewer performed competitively at longer distances. We provide all datasets, submissions and assessment programs for further study, and provide, as a resource for future benchmarking, a convenient repository of code and data for reproducing the simulation assessments.

DNA methylation modulates transcription factor occupancy chiefly at sites of high intrinsic cell-type variability

Matthew Maurano, Hao Wang, Sam John, Anthony Shafer, Theresa Canfield, Kristen Lee, John A Stamatoyannopoulos

The nuclear genome of every cell harbors millions of unoccupied transcription factor (TF) recognition sequences that harbor methylated cytosines. Although DNA methylation is commonly invoked as a repressive mechanism, the extent to which it actively silences specific TF occupancy sites is unknown. To define the role of DNA methylation in modulating TF binding, we quantified the effect of DNA methyltransferase abrogation on the occupancy patterns of a ubiquitous TF capable of autonomous binding to its target sites in chromatin (CTCF). Here we show that the vast majority of unoccupied, methylated CTCF recognition sequences remain unbound upon depletion of DNA methylation. Rather, methylation-regulated binding is restricted to a small fraction of elements that exhibit high intrinsic variability in CTCF occupancy across cell types. Our results suggest that DNA methylation is not a major groundskeeper of genomic transcription factor occupancy landscapes, but rather a specialized mechanism for stabilizing epigenetically labile sites.