HartfieldetalHCVAnalysis

README.TXT FOR SIMULATION FILES. This file is the source code for the Phylogenetic analysis used in the Hartfield et al. paper "Evidence that HCV genome partly controls infection outcome". Simulations are written in R (http://www.r-project.org/). Files included are: - HeritibilitySim.R: Simulating trait transmission along a tree. - Randomisation.R: Analysis of the true and randomised tipset data to determine estimates of phylogenetic signal. - BT_Rand.sh: Shell script to automate analysis of randomisation tip analysis. - command.txt: File needed to run BayesTraits analysis from shell prompt. - G1HITS_state.txt: Example tipset file (chronic/clearer outcome for genotype 1 data). See blurb at start of each program for description and how to execute. Please also note that R code uses the 'ape' package for R, which needs to be installed prior to execution. The full analysis also uses the BayesTraits package, available from http://www.evolution.rdg.ac.uk/BayesTraits.html. Comments should be sent to Matthew Hartfield ([email protected])

Viral Quasispecies Assembly via Maximal Clique Enumeration

<div><p>Virus populations can display high genetic diversity within individual hosts. The intra-host collection of viral haplotypes, called viral quasispecies, is an important determinant of virulence, pathogenesis, and treatment outcome. We present HaploClique, a computational approach to reconstruct the structure of a viral quasispecies from next-generation sequencing data as obtained from bulk sequencing of mixed virus samples. We develop a statistical model for paired-end reads accounting for mutations, insertions, and deletions. Using an iterative maximal clique enumeration approach, read pairs are assembled into haplotypes of increasing length, eventually enabling global haplotype assembly. The performance of our quasispecies assembly method is assessed on simulated data for varying population characteristics and sequencing technology parameters. Owing to its paired-end handling, HaploClique compares favorably to state-of-the-art haplotype inference methods. It can reconstruct error-free full-length haplotypes from low coverage samples and detect large insertions and deletions at low frequencies. We applied HaploClique to sequencing data derived from a clinical hepatitis C virus population of an infected patient and discovered a novel deletion of length 357±167 bp that was validated by two independent long-read sequencing experiments. HaploClique is available at <a href="https://github.com/armintoepfer/haploclique" target="_blank">https://github.com/armintoepfer/haploclique</a>. A summary of this paper appears in the proceedings of the RECOMB 2014 conference, April 2-5.</p></div

Large deletion estimates.

<p>Estimated deletion size deviation and false negative rate for different true deletion sizes of (A) 100, (B) 500, and (C) 1000 bp. For each deletion length and each coverage of 5, 12, 24, 48, 96, and 144×, a boxplot summarizes the deviations of the estimated to the true deletion size in 100 simulated samples. The blue line represents the number of false negative predicted deletions in each of the 100 samples.</p

Max-clique enumeration and edge definitions.

<p>(A) Example of a read alignment graph based on the insert size criterion. Alignments of read pairs are shown in gray and the corresponding nodes in the graph representation are depicted in blue. The four bottom-most alignment pairs stem from a haplotype harboring a deletion (shown in orange in the reference genome) and therefore display a larger insert size than the remaining alignment pairs. Note that the four deletion-indicating alignment pairs form a max-clique (circled in orange). (B) Illustration of the compatible gaps condition of the sequence similarity criterion. Two reads and are aligned against the reference (left). This induces a direct read-to-read alignment of and (right). Case (1): No gaps in the reference alignments lead to a gapless read-to-read alignment, which renders the pair of reads an edge candidate. Case (2): Gaps in the reference alignment lead to gaps in the read-to-read alignment, excluding the possibility of an edge. See also <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003515#pcbi.1003515.s006" target="_blank">Figure S6</a> in the appendix for more complicated cases involving gaps.</p

Global haplotype assembly comparison.

<p>Global haplotype assembly comparison of HaploClique with the software packages ShoRAH <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003515#pcbi.1003515-Zagordi3" target="_blank">[33]</a>, PredictHaplo <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003515#pcbi.1003515-Prabhakaran1" target="_blank">[14]</a>, and QuRe <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003515#pcbi.1003515-Prosperi1" target="_blank">[16]</a>. We report the estimated variant frequencies and, in parenthesis, the maximal length of the reconstructed haplotypes relative to the genome length, for each of the five variants. In the remaining columns, the average error rate (computed as the number of mistaken nucleotides, divided by the length of the haplotype computed), the total number of reconstructed haplotypes, and the precision (percentage of perfectly reconstructed haplotypes weighted by the respective estimated frequency) are reported. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003515#s5" target="_blank">Methods</a> for more details on frequency estimation.</p

Global haplotype assembly results.

<p>Minimum, maximum, and mean read lengths (A) and the total number of reads (B) for the global haplotype assembly of the lab-mix, for the first 13 and the last iteration (30).</p

Accumulation of Deleterious Passenger Mutations Is Associated with the Progression of Hepatocellular Carcinoma

<div><p>In hepatocellular carcinoma (HCC), somatic genome-wide DNA mutations are numerous, universal and heterogeneous. Some of these somatic mutations are drivers of the malignant process but the vast majority are passenger mutations. These passenger mutations can be deleterious to individual protein function but are tolerated by the cell or are offset by a survival advantage conferred by driver mutations. It is unknown if these somatic deleterious passenger mutations (DPMs) develop in the precancerous state of cirrhosis or if it is confined to HCC. Therefore, we studied four whole-exome sequencing datasets, including patients with non-cirrhotic liver (n = 12), cirrhosis without HCC (n = 6) and paired HCC with surrounding non-HCC liver (n = 74 paired samples), to identify DPMs. After filtering out putative germline mutations, we identified 187±22 DPMs per non-diseased tissue. DPMs number was associated with liver disease progressing to HCC, independent of the number of exonic mutations. Tumours contained significantly more DPMs compared to paired non-tumour tissue (258–293 per HCC exome). Cirrhosis- and HCC-associated DPMs do not occur predominantly in specific genes, chromosomes or biological pathways and the effect on tumour biology is presently unknown. Importantly, for the first time we have shown a significant increase in DPMs with HCC.</p></div

Summary data of publicly-available WES datasets used in this study.

<p>Summary data of publicly-available WES datasets used in this study.</p

Summary statistics for normalised DPMs between datasets.

<p>Summary statistics for normalised DPMs between datasets.</p

Transcriptome characterization at the single cell level of viral specific CD8+ T cells

<p>Transcriptome-level characterization of the immune response during viral infection can reveal key mechanisms that underpin the activation and status of cytotoxic CD8+ T Cells (CTL). Once stimulated with antigen CTLs proliferate and differentiate generating a heterogeneous progeny. The advent of single cell analyses, has enabled tracking of the evolving CTLs in human samples during viral infections. We quantified the single cell transcriptome of 81 CTLs identified from a subject with chronic HCV infection. All cells are specific for HLA-I A0201 restricted epitope CINGVCWTV. Single CTLs were obtained from PBMC and from cell line derived from the same patient (unstimulated and following antigen re-stimulation). We analysed the difference between the three groups of single cells using a list of genes previously associated with CTL functions. Genes associated with cytotoxic response (IFN-g, perforin, granzyme B) were highly expressed in cell lines, but not in PBMC derived CTLs. Using unsupervised clustering analysis we identified co-expression clusters within each group. By filtering out the cell-cycle related genes (a major co-founder), we revealed that cells from the restimulated cell line showed a significantly higher level of heterogeneity (p-value <0.0001). The chemokine cell receptors (CCL4, CXCR4) were among the most variable genes.</p