SAM Files Research Articles

PhaseAll: a simple tool for read-based allele phasing

The currently used genome assembly algorithms do not provide for allele phasing. This can lead to the loss of important information about the genotype of diploid and polyploid individuals. Here we introduce PhaseAll, a simple tool for allele phasing based on short reads obtained by second-generation sequencing. As input data, the tool takes paired reeds in SAM format. PhaseAll iterates sequentially through each alignment position. When a polymorphic position (SNP, insertion or deletion) is first encountered, a unique mutation is written to each allele. For each subsequent polymorphic position, a test is made to verify whether it is located on the same pair of reads (one DNA fragment) as the previous one. If two mutations are located on the same fragment, they are considered to belong to the same allele. If no fragments are found that connected at least one pair of neighboring polymorphic positions, an X is written in the allele sequences. This means that the alleles can swap at this position.
 PhaseAll is written in python 3. SAM files are processed using the pysam library. PhaseAll is designed to separate only two alleles. To avoid possible sequencing errors, the user can set a read depth threshold below which the polymorphic position will be skipped. Some indels can cause errors in allele phasing, so PhaseAll has an option to skip indels for more accurate SNP reconstruction. The tool was tested on sequences of agrobacterial origin in the Camellia L. genome in more than 100 samples. PhaseAll is available for download on the GitHub: https://github.com/pzhurbenko/PhaseAll
 The research was supported by RSF (project No. 21-14-00050).

J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments

BackgroundThe combined effects of biological variability and measurement-related errors on cancer sequencing data remain largely unexplored. However, the spatio-temporal simulation of multi-cellular systems provides a powerful instrument to address this issue. In particular, efficient algorithmic frameworks are needed to overcome the harsh trade-off between scalability and expressivity, so to allow one to simulate both realistic cancer evolution scenarios and the related sequencing experiments, which can then be used to benchmark downstream bioinformatics methods.ResultWe introduce a Julia package for SPAtial Cancer Evolution (J-SPACE), which allows one to model and simulate a broad set of experimental scenarios, phenomenological rules and sequencing settings.Specifically, J-SPACE simulates the spatial dynamics of cells as a continuous-time multi-type birth-death stochastic process on a arbitrary graph, employing different rules of interaction and an optimised Gillespie algorithm. The evolutionary dynamics of genomic alterations (single-nucleotide variants and indels) is simulated either under the Infinite Sites Assumption or several different substitution models, including one based on mutational signatures. After mimicking the spatial sampling of tumour cells, J-SPACE returns the related phylogenetic model, and allows one to generate synthetic reads from several Next-Generation Sequencing (NGS) platforms, via the ART read simulator. The results are finally returned in standard FASTA, FASTQ, SAM, ALN and Newick file formats.ConclusionJ-SPACE is designed to efficiently simulate the heterogeneous behaviour of a large number of cancer cells and produces a rich set of outputs. Our framework is useful to investigate the emergent spatial dynamics of cancer subpopulations, as well as to assess the impact of incomplete sampling and of experiment-specific errors. Importantly, the output of J-SPACE is designed to allow the performance assessment of downstream bioinformatics pipelines processing NGS data. J-SPACE is freely available at: https://github.com/BIMIB-DISCo/J-Space.jl.

Poly_NumtS_430 or HSA_NumtS_587 observed in massively parallel sequencing of the mitochondrial HV1 and HV2 regions

An increasing number of studies on massively parallel sequencing of mitochondrial DNA (mtDNA) have been reporting identification of various types of noise or off-target sequences. Herein, we report that an off-target haplotype (sequence length 192 bp) observed in MiSeq data of mtDNA at nucleotide position 16,209–16,400 was likely caused by polymorphic nuclear mitochondrial DNA sequences (NumtS). Buccal DNA samples from Volunteers #001–004 and Control DNA 007 were amplified with our multiplex system of the B (15,998−16,172), C (16,209−16,400), and E (30−289) regions using 2000 copies of mtDNA. A sample index was added using a Nextera XT index kit, and MiSeq Reagent Nano Kit v2 was used in 2 × 251 cycles on a MiSeq FGx. FASTQ files were analyzed by CLC Genomics Workbench 21.0.3. The extracted SAM files were analyzed using our original Excel macro to sum up the read counts as the phased variant calls for each region. An off-target haplotype differing at 19 sites against the revised Cambridge reference sequence was observed in Volunteer #001 (4 in 10 MiSeq data), Volunteer #002 (2 in 9), and Control DNA 007 (6 in 9). In a BLAST search, the sequence of the off-target haplotype matched perfectly to three polymorphic NumtS (Poly_NumtS_430 [KM281528.1], HSA_NumtS_587 [HE613849.1], and nuclear fossil [S80333.1]) and BAC clone of chromosome 11 (AC107937.2). The sequence also matched perfectly to a Filipino mtDNA (KC993973.1) which was inferred as a chimeric sequence of mtDNA and the HSA_NumtS_587. The sequence of the off-target haplotype was not contained in the latest human reference genome sequence (GRCh38.p13). In a phylogenetic tree, the off-target haplotype was genetically distant from modern human mtDNA and not directly connected to them. In conclusion, observed off-target haplotype amplified by our multiplex system was likely caused by Poly_NumtS_430 or HSA_NumtS_587.

Chromosome-scale genome sequencing, assembly and annotation of six genomes from subfamily Leishmaniinae

We provide the raw and processed data produced during the genome sequencing of isolates from six species of parasites from the sub-family Leishmaniinae: Leishmania martiniquensis (Thailand), Leishmania orientalis (Thailand), Leishmania enriettii (Brazil), Leishmania sp. Ghana, Leishmania sp. Namibia and Porcisia hertigi (Panama). De novo assembly was performed using Nanopore long reads to construct chromosome backbone scaffolds. We then corrected erroneous base calling by mapping short Illumina paired-end reads onto the initial assembly. Data has been deposited at NCBI as follows: raw sequencing output in the Sequence Read Archive, finished genomes in GenBank, and ancillary data in BioSample and BioProject. Derived data such as quality scoring, SAM files, genome annotations and repeat sequence lists have been deposited in Lancaster University’s electronic data archive with DOIs provided for each item. Our coding workflow has been deposited in GitHub and Zenodo repositories. This data constitutes a resource for the comparative genomics of parasites and for further applications in general and clinical parasitology.

A resampling strategy for studying robustness in virus detection pipelines

Next-generation sequencing is regularly used to identify viral sequences in DNA or RNA samples of infected hosts. A major step of most pipelines for virus detection is to map sequence reads against known virus genomes. Due to small differences between the sequences of related viruses, and due to several biological or technical errors, mapping underlies uncertainties. As a consequence, the resulting list of detected viruses can lack robustness.A new approach for generating artificial sequencing reads together with a strategy of resampling from the original findings is proposed that can help to assess the robustness of the originally identified list of viruses. From the original mapping result in form of a SAM file, a set of statistical distributions are derived. These are used in the resampling pipeline to generate new artificial reads which are again mapped versus the reference genomes. By summarizing the resampling procedure, the analyst receives information about whether the presence of a particular virus in the sample gains or losses evidence, and thus about the robustness of the original mapping list but also that of individual viruses in this list. To judge robustness, several indicators are derived from the resampling procedure such as the correlation between original and resampling read counts, or the statistical detection of outliers in the differences of read counts. Additionally, graphical illustrations of read count shifts via Sankey diagrams are provided.To demonstrate the use of the new approach, the resampling approach is applied to three real-world data samples, one of them with laboratory-confirmed Influenza sequences, and to artificially generated data where virus sequences have been spiked into the sequencing data of a host. By applying the resampling pipeline, several viruses drop from the original list while new viruses emerge, showing robustness of those viruses that remain in the list.The evaluation of the new approach shows that the resampling approach is helpful to analyze the viral content of a biological sample, to rate the robustness of original findings and to better show the overall distribution of findings. The method is also applicable to other virus detection pipelines based on read mapping.

SSRgenotyper: A simple sequence repeat genotyping application for whole-genome resequencing and reduced representational sequencing projects.

PREMISEMany programs can identify simple sequence repeat (SSR) motifs in genomic data. SSRgenotyper extends SSR identification to en masse genotyping from resequencing data for diversity panels and linkage mapping populations.METHODS AND RESULTSSSRgenotyper will find and genotype SSRs from SAM files and an SSR reference FASTA. Several outputs are possible, including a simple table with the SSR marker name, position, and SSR alleles, defined by the repeat number of the repeat motif. Specific output files include a GENEPOP‐formatted file for downstream genetic diversity analyses and a traditional A, H, B mapping file output that is phased to the parents of the population for biparental linkage map construction. Linkage maps produced using SSRgenotyper genotypes were highly collinear with physical maps and correctly inferred known phylogenies.CONCLUSIONSSSRgenotyper provides an easy‐to‐use, accurate, and scalable SSR genotyping platform for whole‐genome resequencing data. SSRgenotyper is freely available at https://github.com/dlewis27/SSRgenotyper.

Lossy Compression of Quality Values in Sequencing Data.

The dropping cost of sequencing human DNA has allowed for fast development of several projects around the world generating huge amounts of DNA sequencing data. This deluge of data has run up against limited storage space, a problem that researchers are trying to solve through compression techniques. In this study we address the compression of SAM files, the standard output files for DNA alignment. We specifically study lossy compression techniques used for quality values reported in the SAM file and analyze the impact of such lossy techniques on the CRAM format. We present a series of experiments using a data set corresponding to individual NA12878 with three different fold coverages. We introduce a new lossy model, dynamic binning, and compare its performance to other lossy techniques, namely Illumina binning, LEON and QVZ. We analyze the compression ratio when using CRAM and also study the impact of the lossy techniques on SNP calling. Our results show that lossy techniques allow a better CRAM compression ratio. Furthermore, we show that SNP calling performance is not negatively affected and may even be boosted.

Abstract LB-212: FCRF: An efficient algorithm for detecting circular fusion transcript from RNA-Seq data

Abstract Fusion circular RNAs (f-circRNAs) are circular RNAs derived from rearranged genome translocations. Their formation is caused by back-splicing happening to the genome regions of aberrant chromosomal translocation. Their cancer-promoting roles have been implicated in recent studies. One of them adopted both in vivo and in vitro models to demonstrate the oncogenic roles of f-circRNAs including promoting cellular transformation, cell viability and resistance to treatment. Therefore, it is suggested that accurate identification of f-circRNA will contribute to cancer diagnosis and therapy. But until now there is only one tool called Acfs (accurate circRNA finder suite) ever mentioned detecting f-circRNAs whose primary job is to detect normal circRNAs and only 10 f-circRNAs have been tested for verification without considering realistic sequencing problems. Here, we proposed the first algorithm, FCRF (Fusion CircRNA Finder), to identify f-circRNAs from RNA-Seq data. The SAM file aligned by BWA was used as input, and paired fusion junction site locations would be output. After pre-processing and correcting the original data in consideration of the sequencing fault tolerance, we divided the candidates into balance set and unbalance set according to the relative aligned length. For balance set, we extracted reads from non-homologous chromosomes owing to the fusion events and further detected the paired clipping signals specific to fusion junctions. On the other hand, for unbalance set, we could not use the paired signal because BWA only reported one alignment for them. So we handled them with local similar sequence considering that each junction read has similar sequences with the other read spanning the same junction. Finally we searched all the candidate fusion junctions in accord with the threshold window size and cyclization characteristics, and then recorded two special junction site locations for each fusion-circRNA. We conducted a series of simulation experiments to evaluate our algorithm. The proposed algorithm FCRF achieved 95% on precision and 85% on sensitivity where Acfs output nothing from our improved dataset. Note: This abstract was not presented at the meeting. Citation Format: Han Yu, Xuanping Zhang, Yanfang Guan, Yongsheng Chen, Xiao Xiao, Jiayin Wang. FCRF: An efficient algorithm for detecting circular fusion transcript from RNA-Seq data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-212.

A comparison of three programming languages for a full-fledged next-generation sequencing tool

BackgroundelPrep is an established multi-threaded framework for preparing SAM and BAM files in sequencing pipelines. To achieve good performance, its software architecture makes only a single pass through a SAM/BAM file for multiple preparation steps, and keeps sequencing data as much as possible in main memory. Similar to other SAM/BAM tools, management of heap memory is a complex task in elPrep, and it became a serious productivity bottleneck in its original implementation language during recent further development of elPrep. We therefore investigated three alternative programming languages: Go and Java using a concurrent, parallel garbage collector on the one hand, and C++17 using reference counting on the other hand for handling large amounts of heap objects. We reimplemented elPrep in all three languages and benchmarked their runtime performance and memory use.ResultsThe Go implementation performs best, yielding the best balance between runtime performance and memory use. While the Java benchmarks report a somewhat faster runtime than the Go benchmarks, the memory use of the Java runs is significantly higher. The C++17 benchmarks run significantly slower than both Go and Java, while using somewhat more memory than the Go runs. Our analysis shows that concurrent, parallel garbage collection is better at managing a large heap of objects than reference counting in our case.ConclusionsBased on our benchmark results, we selected Go as our new implementation language for elPrep, and recommend considering Go as a good candidate for developing other bioinformatics tools for processing SAM/BAM data as well.

SU54 - A PILOT METHYLATED DNA IMMUNOPRECIPITATION SEQUENCING: HYPERMETHYLATION OF THE DEATH-ASSOCIATED PROTEIN 3 (DAP3) AND MIR2110 IN FIBROMYALGIA ASSOCIATED WITH CHILD ABUSE HISTORY AMONG AFRICAN AMERICAN FEMALES

Background Epigenetics, such as DNA methylation, has been hypothesized as one possible mechanism to explain the association between adverse childhood experiences and later health problems. The goal of this study was to identify genome-wide DNA methylation markers from peripheral blood associated with child abuse history among 6 African American adult females with Fibromyalgia Syndrome (FMS) using Methylated DNA Immuno-Precipitation sequencing (MeDIP-Seq). Methods Data were from a fibromyalgia, natural history study approved by the MedStar Health IRB. FMS was diagnosed using the 1990 or the 2010 American College of Rheumatology criteria and the Widespread Pain Index. Pain, fatigue, anxiety, depression, and cognitive function were assessed. A detailed abuse history was obtained, including age at the time and type of abuse (i.e., emotional, physical, or rape). A total of 6 FMS African American females, including 4 with any abuse before age 18 (Abused FMS female; AbFF) and 2 without (Non-abused FMS female; NFF), were selected in this study. Using peripheral blood collected from the subjects’ genomic DNA was extracted. DNA fragments containing methylated CpG were enriched and sequenced in the HiSeq-2500 system using MeDIP-Seq. Reads were aligned to the reference human genome of hg38. Generated sam files were sorted according to chromosomal position. Peaks were called using MACS version 2.1.1.20160309, to identify methylated DNA regions. DiffBind software package was used to perform clustering, Principal Component Analysis (PCA), and differential peak interval analysis with False Discovery Rate (FDR) of 0.05 cutoff. Results All of the 6 subjects with FMS met both the 1990 and 2010 criteria, except 1 NFF who only met the 1990 criteria. Age ranged from 45 to 56 years (mean=53.5, SD=4.23). The majority (66.7%) did not have a college education, unemployed, or divorced or widowed. Among the 4 AFFs, 1 reported rape + physical abuse, 2 reported rape only, and 1 reported emotional abuse. No significant differences were found in clinical symptoms or characteristics between AbFFs and NFFs. The number of methylated intervals per each subject ranged from 234,842 to 454,508. With the union of all these intervals, we obtained 412,226 methylated intervals, overall. Both of clustering analysis and PCA clearly separated AbFFs and NFFs. The differential analysis with FDR of 0.05 identified 15 intervals hypermethylated for AFPs and another 15 intervals hypermethylated for NFPs. After excluding ambiguous regions to interpret (e.g., repetitive DNA regions such as ribosomal repeating regions, the human alpha satellite repeat region) we identified DAP3 and mir2110 hypermethylated for AbFFs compared to NFFs. Discussion This study suggests hypermethylation in DAP3 and mir2110 could be used as candidates for novel biomarkers associated with child abuse history in African American females with fibromyalgia. Epigenetic inactivation patterns, especially one in DAP3, are found in numerous tumor cells and negatively affect cancer prognosis. Reduced DAP3 expression affects its normal regulation of cell respiration and apoptosis induced by oxidative stress, which is important of cell death pathways related to the ischemia-reperfusion injury. Further study is required to confirm this finding with more clinical samples.

QVZ: lossy compression of quality values.

MOTIVATION Recent advancements in sequencing technology have led to a drastic reduction in the cost of sequencing a genome. This has generated an unprecedented amount of genomic data that must be stored, processed and transmitted. To facilitate this effort, we propose a new lossy compressor for the quality values presented in genomic data files (e.g. FASTQ and SAM files), which comprise roughly half of the storage space (in the uncompressed domain). Lossy compression allows for compression of data beyond its lossless limit. RESULTS The proposed algorithm QVZ exhibits better rate-distortion performance than the previously proposed algorithms, for several distortion metrics and for the lossless case. Moreover, it allows the user to define any quasi-convex distortion function to be minimized, a feature not supported by the previous algorithms. Finally, we show that QVZ-compressed data exhibit better performance in the genotyping than data compressed with previously proposed algorithms, in the sense that for a similar rate, a genotyping closer to that achieved with the original quality values is obtained. AVAILABILITY AND IMPLEMENTATION QVZ is written in C and can be downloaded from https://github.com/mikelhernaez/qvz. CONTACT mhernaez@stanford.edu or gmalysa@stanford.edu or iochoa@stanford.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

GeneComp, a new reference-based compressor for SAM files.

The affordability of DNA sequencing has led to unprecedented volumes of genomic data. These data must be stored, processed, and analyzed. The most popular format for genomic data is the SAM format, which contains information such as alignment, quality values, etc. These files are large (on the order of terabytes), which necessitates compression. In this work we propose a new reference-based compressor for SAM files, which can accommodate different levels of compression, based on the specific needs of the user. In particular, the proposed compressor GeneComp allows the user to perform lossy compression of the quality scores, which have been proven to occupy more than half of the compressed file (when losslessly compressed). We show that the proposed compressor GeneComp overall achieves better compression ratios than previously proposed algorithms when working on lossless mode.

CoverageAnalyzer (CAn): A Tool for Inspection of Modification Signatures in RNA Sequencing Profiles.

Combination of reverse transcription (RT) and deep sequencing has emerged as a powerful instrument for the detection of RNA modifications, a field that has seen a recent surge in activity because of its importance in gene regulation. Recent studies yielded high-resolution RT signatures of modified ribonucleotides relying on both sequence-dependent mismatch patterns and reverse transcription arrests. Common alignment viewers lack specialized functionality, such as filtering, tailored visualization, image export and differential analysis. Consequently, the community will profit from a platform seamlessly connecting detailed visual inspection of RT signatures and automated screening for modification candidates. CoverageAnalyzer (CAn) was developed in response to the demand for a powerful inspection tool. It is freely available for all three main operating systems. With SAM file format as standard input, CAn is an intuitive and user-friendly tool that is generally applicable to the large community of biomedical users, starting from simple visualization of RNA sequencing (RNA-Seq) data, up to sophisticated modification analysis with significance-based modification candidate calling.

FBB: a fast Bayesian-bound tool to calibrate RNA-seq aligners.

Despite RNA-seq reads provide quality scores that represent the probability of calling a correct base, these values are not probabilistically integrated in most alignment algorithms. Based on the quality scores of the reads, we propose to calculate a lower bound of the probability of alignment of any fast alignment algorithm that generates SAM files. This bound is called Fast Bayesian Bound (FBB) and serves as a canonical reference to compare alignment results across different algorithms. This Bayesian Bound intends to provide additional support to the current state-of-the-art aligners, not to replace them. We propose a feasible Bayesian bound that uses quality scores of the reads to align them to a genome of reference. Two theorems are provided to efficiently calculate the Bayesian bound that under some conditions becomes the equality. The algorithm reads the SAM files generated by the alignment algorithms using multiple command option values. The program options are mapped into the FBB reference values, and all the aligners can be compared respect to the same accuracy values provided by the FBB. Stranded paired read RNA-seq data was used for evaluation purposes. The errors of the alignments can be calculated based on the information contained in the distance between the pairs given by Theorem 2, and the alignments to the incorrect strand. Most of the algorithms (Bowtie, Bowtie 2, SHRiMP2, Soap 2, Novoalign) provide similar results with subtle variations. Current version of the FBB software is provided at https://bitbucket.org/irenerodriguez/fbb CONTACT: rhuerta@ucsd.eduSupplementary information: Supplementary data are available at Bioinformatics online.

CSAM: Compressed SAM format.

Next generation sequencing machines produce vast amounts of genomic data. For the data to be useful, it is essential that it can be stored and manipulated efficiently. This work responds to the combined challenge of compressing genomic data, while providing fast access to regions of interest, without necessitating decompression of whole files. We describe CSAM (Compressed SAM format), a compression approach offering lossless and lossy compression for SAM files. The structures and techniques proposed are suitable for representing SAM files, as well as supporting fast access to the compressed information. They generate more compact lossless representations than BAM, which is currently the preferred lossless compressed SAM-equivalent format; and are self-contained, that is, they do not depend on any external resources to compress or decompress SAM files. An implementation is available at https://github.com/rcanovas/libCSAM CONTACT: canovas-ba@lirmm.frSupplementary Information: Supplementary data is available at Bioinformatics online.

SamExploreR: exploring reproducibility and robustness of RNA-seq results based on SAM files.

Data from RNA-seq experiments provide us with many new possibilities to gain insights into biological and disease mechanisms of cellular functioning. However, the reproducibility and robustness of RNA-seq data analysis results is often unclear. This is in part attributed to the two counter acting goals of (i) a cost efficient and (ii) an optimal experimental design leading to a compromise, e.g. in the sequencing depth of experiments. We introduce an R package called samExploreR that allows the subsampling (m out of n bootstraping) of short-reads based on SAM files facilitating the investigation of sequencing depth related questions for the experimental design. Overall, this provides a systematic way for exploring the reproducibility and robustness of general RNA-seq studies. We exemplify the usage of samExploreR by studying the influence of the sequencing depth and the annotation on the identification of differentially expressed genes. samExploreR is available as an R package from Bioconductor. v@bio-complexity.comSupplementary information: Supplementary data are available at Bioinformatics online.

QVZ: lossy compression of quality values.

Recent advancements in sequencing technology have led to a drastic reduction in the cost of sequencing a genome. This has generated an unprecedented amount of genomic data that must be stored, processed and transmitted. To facilitate this effort, we propose a new lossy compressor for the quality values presented in genomic data files (e.g. FASTQ and SAM files), which comprise roughly half of the storage space (in the uncompressed domain). Lossy compression allows for compression of data beyond its lossless limit. The proposed algorithm QVZ exhibits better rate-distortion performance than the previously proposed algorithms, for several distortion metrics and for the lossless case. Moreover, it allows the user to define any quasi-convex distortion function to be minimized, a feature not supported by the previous algorithms. Finally, we show that QVZ-compressed data exhibit better performance in the genotyping than data compressed with previously proposed algorithms, in the sense that for a similar rate, a genotyping closer to that achieved with the original quality values is obtained. QVZ is written in C and can be downloaded from https://github.com/mikelhernaez/qvz. mhernaez@stanford.edu or gmalysa@stanford.edu or iochoa@stanford.edu Supplementary data are available at Bioinformatics online.

Dynamic Regulation of Schwann Cell Enhancers after Peripheral Nerve Injury

Myelination of the peripheral nervous system is required for axonal function and long term stability. After peripheral nerve injury, Schwann cells transition from axon myelination to a demyelinated state that supports neuronal survival and ultimately remyelination of axons. Reprogramming of gene expression patterns during development and injury responses is shaped by the actions of distal regulatory elements that integrate the actions of multiple transcription factors. We used ChIP-seq to measure changes in histone H3K27 acetylation, a mark of active enhancers, to identify enhancers in myelinating rat peripheral nerve and their dynamics after demyelinating nerve injury. Analysis of injury-induced enhancers identified enriched motifs for c-Jun, a transcription factor required for Schwann cells to support nerve regeneration. We identify a c-Jun-bound enhancer in the gene for Runx2, a transcription factor induced after nerve injury, and we show that Runx2 is required for activation of other induced genes. In contrast, enhancers that lose H3K27ac after nerve injury are enriched for binding sites of the Sox10 and early growth response 2 (Egr2/Krox20) transcription factors, which are critical determinants of Schwann cell differentiation. Egr2 expression is lost after nerve injury, and many Egr2-binding sites lose H3K27ac after nerve injury. However, the majority of Egr2-bound enhancers retain H3K27ac, indicating that other transcription factors maintain active enhancer status after nerve injury. The global epigenomic changes in H3K27ac deposition pinpoint dynamic changes in enhancers that mediate the effects of transcription factors that control Schwann cell myelination and peripheral nervous system responses to nerve injury.

The Scramble conversion tool.

Motivation: The reference CRAM file format implementation is in Java. We present ‘Scramble’: a new C implementation of SAM, BAM and CRAM file I/O.Results: The C implementation of for CRAM is 1.5–1.7× slower than BAM at decoding but 1.8–2.6× faster at encoding. We see file size savings of 34–55%.Availability and implementation: Source code is available at http://sourceforge.net/projects/staden/files/io_lib/ under the BSD software licence.Contact: jkb@sanger.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

PSIM: pattern-based read simulator for RNA-seq analysis

Next-generation sequencing technologies (NGS) require mapping tools that are fundamental for their application. These are evaluated by the level of accuracy to be matched and read at the original location. Evaluation increases the need for a simulator to generate reads with their locations and errors, as with indel. In this paper, we propose a simulator, PSIM, that generating a set of artificial RNA segments(reads) with the expression level and errors based on a pattern-based SAM file. PSIM adopts the contour line transpose and interval section shuffle methods to generate a similar expression level. In addition, we show the similarity between a profile contour of synthesized data and a reference sequence.