Abstract: Motivation: High-throughput sequencing (HTS) has revolutionized gene regulation studies and is now fundamental for the detection of protein–DNA and protein–RNA binding, as well as for measuring RNA expression. With increasing variety and sequencing depth of HTS datasets, the need for more flexible and memory-efficient tools to analyse them is growing. Results: We describe Pyicos, a powerful toolkit for the analysis of mapped reads from diverse HTS experiments: ChIP-Seq, either punctuated or broad signals, CLIP-Seq and RNA-Seq. We prove the effectiveness of Pyicos to select for significant signals and show that its accuracy is comparable and sometimes superior to that of methods specifically designed for each particular type of experiment. Pyicos facilitates the analysis of a variety of HTS datatypes through its flexibility and memory efficiency, providing a useful framework for data integration into models of regulatory genomics. Availability: Open-source software, with tutorials and protocol files, is available at http://regulatorygenomics.upf.edu/pyicos or as a Galaxy server at http://regulatorygenomics.upf.edu/galaxy Contact:  eduardo.eyras@upf.edu Supplementary Information:  Supplementary data are available at Bioinformatics online.

Abstract: Introduction: In acute myeloid leukemia (AML), the karyotype and the molecular mutation profile are the strongest parameters for classification and prognostication. Yet, diagnostic analyses rely on chromosome analysis and sequencing of a constantly growing number of genes. Aim: To evaluate whether whole exome sequencing (WES) can reliably identify copy number states and molecular mutations in a single-step procedure. Patients and Methods: The cohort included 24 AML with an aberrant karyotype at initial diagnosis (ID) who achieved cytogenetic remission (CR) after chemotherapy. Patients showed complex karyotype (n=6), 11q23/MLL-rearrangement (n=4), t(15;17)(q24;q21) (n=4), inv(16)(p13q22) (n=4), t(8;21)(q22;q22) (n=3), and 3q26/EVI1-rearrangement (n=3). For WES DNA was extracted from bone marrow and treated with the TruSeq Exome enrichment kit targeting 201,071 exons. 2x100 bp paired-end sequencing was performed on an Illumina HiSeq machine (Illumina, San Diego, CA) at Fasteris (Geneva, Switzerland). After mapping the sequenced reads with Burrows-Wheeler Aligner [Li & Durbin, Bioinformatics, 2009], variants where called with GATK [McKenna et al., Genome Res., 2010] and copy number variations (CNV) were detected by Excavator [Magi et al., 2013, Genome Biol.]. For validation of the detected variants, 21 leukemia related genes were screened by amplicon sequencing (Illumina MiSeq, or Roche 454, Branford, CT). Array-based comparative genomic hybridization (aCGH) using 12x270K microarrays (Roche NimbleGen, Madison, WI) or 4 x 180K microarray slides (Agilent Technologies, Santa Clara, CA) was performed on all samples. We called CNV using default settings as well as fixed thresholds on the probe medians (0.3 for gains and -0.5 for losses on probe medians and at least 10 probes per segment). Results: The targeted regions were covered by 86 reads on average, while 90% of the bases were covered by at least 15 reads. By comparing ID and CR we detected an average of 15 somatic single nucleotide variants and short indels per patient (range 4-25), affecting 303 genes in total, including genes involved in leukemogenesis. After excluding polymorphisms we screened the mutated genes for recurrence among all cases. Four genes were mutated in at least 3 samples: WT1 (n=5), TP53 (n=4), NRAS (n=3) and TNS1 (n=3). Fourteen genes were mutated in 2 samples: ASXL2, DSCAM, GATA2, IDH2, KIT, OR4C5, POU4F1, LOC93432, RPTOR, SMC1A, SYNE2, TET2, TTN and USP9X. Mutations in OR4C5, LOC93432, SYNE2, TTN and USP9X have not been associated with AML yet. They were rated as damaging according to the SIFT algorithm [Ng and Henikoff, Genome Res., 2003]. In a prior diagnostic work-up 21 different genes had been screened and revealed 16 mutations affecting 7 genes. WES identified 14 mutations correctly (the 2 remaining mutations were covered by reads only insufficiently) and did not call any mutation in genes classified as negative in the routine diagnostic work-up. We further compared CNV derived from WES and aCGH in all 24 patients. Gains and losses detected by aCGH involved 2.65 and 1.40 billion bp, respectively. 96% of bp involved in these CNV were also detected by WES. Of the regions in which WES could not reproduce CNV calls, 15% did not contain exons. WES called gains and losses covering in total 2.56 and 1.47 billion bp, respectively. With aCGH we detected 98% of the gains and 86% of the losses. Regions missed by aCGH did show concordant signal that did not pass the fixed thresholds. However, while relaxing the thresholds to default settings, aCGH reproduces 99% of the WES results. Thus, an excellent concordance was observed (R = 0.99, p 〈 2.2e-16). We further analysed 19 cytogenetically balanced rearrangements that caused 42 breakpoints in affected chromosomes in 17 patients. As most breakpoints occur in non-coding regions, WES in general is limited in detecting these balanced rearrangements. However, short CNV were detected by WES in 10 cases and confirmed by aCGH. Conclusion: WES was capable of delineating molecular mutation profiles and of robustly detecting copy number states in AML at diagnosis. We suggest that WES in combination with multiplex RT-PCR-based techniques for the detection of recurrent fusion transcripts is a promising approach for a future diagnostic work-up for AML classification and prognostication. This project has been funded by the Seventh Framework Programme (FP7/2007-2013) under grant agreement n. 306242. Disclosures Althammer: MLL Munich Leukemia Laboratory: Employment; Seventh Framework Programme (FP7/2007-2013): Research Funding. de Albuquerque:MLL Munich Leukemia Laboratory: Employment; Seventh Framework Programme (FP7/2007-2013): Research Funding. Nadarajah:MLL Munich Leukemia Laboratory: Employment; Seventh Framework Programme (FP7/2007-2013): Research Funding. Meggendorfer:MLL Munich Leukemia Laboratory: Employment; Seventh Framework Programme (FP7/2007-2013): Research Funding. Schnittger:MLL Munich Leukemia Laboratory: Employment, Equity Ownership; Seventh Framework Programme (FP7/2007-2013): Research Funding. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership; Seventh Framework Programme (FP7/2007-2013): Research Funding. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership; Seventh Framework Programme (FP7/2007-2013): Research Funding. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership; Seventh Framework Programme (FP7/2007-2013): Research Funding.

Abstract: The epigenetic regulation of gene expression involves multiple factors. The synergistic or antagonistic action of these factors has suggested the existence of an epigenetic code for gene regulation. Highthroughput sequencing (HTS) provides an opportunity to explore this code and to build quantitative models of gene regulation based on epigenetic differences between specific cellular conditions. We describe a new computational framework that facilitates the systematic integration of HTS epigenetic data. Our method relates epigenetic signals to expression by comparing two conditions. We show its effectiveness by building a model that predicts with high accuracy significant expression differences between two cell lines, using epigenetic data from the ENCODE project. Our analyses provide evidence for a degenerate epigenetic code, which involves multiple genic regions. In particular, signal changes at the 1st exon, 1st intron, and downstream of the polyadenylation site are found to associate strongly with expression regulation. Our analyses also show a different epigenetic code for intron-less and intron-containing genes. Our work provides a general methodology to do integrative analysis of epigenetic differences between cellular conditions that can be applied to other studies, like cell differentiation or carcinogenesis.