• Tools for identifying features from ENCODE assays
• Integrative analysis across data types
• Quality Metrics for ENCODE data
• Other Tools

Updated 25 July 2012

ChIP-seq Peak Callers

• MACS
  A widely-used, fast, robust ChIP-seq peak-finding algorithm that accounts for the offset in forward-strand and reverse-strand reads to improve resolution and uses a dynamic Poisson distribution to effectively capture local biases in the genome. MACS 1.4 is being used for the current uniform peak calling pipeline.

  Feng J, Liu T, Zhang Y. Using MACS to identify peaks from ChIP-Seq data. Curr Protoc Bioinformatics. 2011 Jun;Chapter 2:Unit 2.14.

  Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008;9(9):R137.

• PeakSeq
  Identifies enriched regions in ChIP-seq type experiments and explicitly compares signal experiments to control experiments.

  Rozowsky J, Euskirchen G, Auerbach RK, Zhang ZD, Gibson T, Bjornson R, Carriero N, Snyder M, Gerstein MB. PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls. Nat Biotechnol. 2009 Jan;27(1):66-75.

• SPP
  A ChIP-seq peak calling algorithm, implemented as an R package, that accounts for the offset in forward-strand and reverse-strand reads to improve resolution, compares enrichment in signal to background or control experiments, and can also estimate whether the available number of reads is sufficient to achieve saturation, meaning that additional reads would not allow identification of additional peaks.

  Kharchenko PV, Tolstorukov MY, Park PJ. Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nat Biotechnol. 2008 Dec;26(12):1351-9.

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RNA-seq Analysis

• FusionSeq
  A computational framework that detects chimeric transcripts from paired-end RNA-seq experiments.

  Sboner A, Habegger L, Pflueger D, Terry S, Chen DZ, Rozowsky JS, Tewari AK, Kitabayashi N, Moss BJ, Chee MS et al. FusionSeq: a modular framework for finding gene fusions by analyzing paired-end RNA-sequencing data. Genome Biol. 2010;11(10):R104.

• IQSeq
  Uses RNA-seq data for isoform quantification.

  Du J, Leng J, Habegger L, Sboner A, McDermott D, Gerstein M. IQSeq: integrated isoform quantification analysis based on next-generation sequencing. PLoS One. 2012;7(1):e29175.

• RSEQtools
  Processes, quantifies, and annotates gene expression data from RNA-seq experiments. Utilizes Mapped Read Format (MRF) for secure and efficient analysis.

  Habegger L, Sboner A, Gianoulis TA, Rozowsky J, Agarwal A, Snyder M, Gerstein M. RSEQtools: a modular framework to analyze RNA-Seq data using compact, anonymized data summaries. Bioinformatics. 2011 Jan 15;27(2):281-3.

• Scripture
  Reconstructs transcriptomes, relying solely on RNA-seq reads and an assembled genome to build a transcriptome ab initio. The statistical methods to estimate read coverage significance are also applicable to other sequencing data. Scripture also has modules for ChIP-seq peak calling.

  Guttman M, Garber M, Levin JZ, Donaghey J, Robinson J, Adiconis X, Fan L, Koziol MJ, Gnirke A, Nusbaum C et al. Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat Biotechnol. 2010 May;28(5):503-10.

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Other

• AlleleSeq
  Quantifies the allele-specific binding and expression at the SNP sites using the RNA-seq, ChIP-seq datasets, and diploid genome sequences.

  Rozowsky J, Abyzov A, Wang J, Alves P, Raha D, Harmanci A, Leng J, Bjornson R, Kong Y, Kitabayashi N et al. AlleleSeq: analysis of allele-specific expression and binding in a network framework. Mol Syst Biol. 2011 Aug 2;7:522.

• Hotspot
  Identifies regions of local enrichment, including peaks, in genomic short-read sequence data. Uses the binomial distribution with a local background model to automatically correct for broad-scale regional differences in tag levels. It is applicable to a wide variety of epigenetic profiling assays, including ChIP-seq and DNase-seq. Hotspot forms the basis of the SPOT data quality metric (described below).

  John S, Sabo PJ, Thurman RE, Sung MH, Biddie SC, Johnson TA, Hager GL, Stamatoyannopoulos JA. Chromatin accessibility pre-determines glucocorticoid receptor binding patterns. Nat Genet. 2011 Mar;43(3):264-8.

• incRNA
  A computational framework that identifies structured RNAs by combining a variety of gene expression data and several sequence-based metrics.

  Lu ZJ, Yip KY, Wang G, Shou C, Hillier LW, Khurana E, Agarwal A, Auerbach R, Rozowsky J, Cheng C et al. Prediction and characterization of noncoding RNAs in C. elegans by integrating conservation, secondary structure, and high-throughput sequencing and array data. Genome Res. 2011 Feb;21(2):276-85.

• Irreproducible Discovery Rate (IDR)
  Measures consistency between replicates in high-throughput experiments. The core IDR R package can be downloaded from the IDR download page. The version of IDR code used for processing ENCODE ChIP-seq data and the commands / manual for running IDR on ChIP-seq data can be found at the IDR: Reproducibility and automatic thresholding of ChIP-seq data page.

  Kundaje A, Li Q, Brown J, Rozowsky J, Harmanci A, Wilder S, Gerstein M, Dunham I, Birney E, Batzoglou S et al. Reproducibility measures for automatic threshold selection and quality control in ChIP-seq datasets. (In Preparation)

  Li Q, Brown JB, Huang H, Bickel PJ. Annals of Applied Statistics. 5(3):1752-1779.

• Wiggler
  Produces normalized genome-wide signal coverage tracks from raw read alignment files. Allows pooling of replicate datasets while allowing for replicate and data-type specific read shifting and smoothing parameters. It can be used to generate signal density maps for ChIP-seq, DNase-seq, FAIRE-seq and MNase-seq data. Wiggler also implicitly models variability in mappability to appropriately normalize signal density and distinguish missing data from true zero signal. Wiggler software is available for download from the align2rawsignal project page.

  Hoffman M, Ernst J, Wilder S, Kundaje A, Harris R, Libbrecht M, Giardine B, Bilmes J, Birney E, Hardison R et al. Integrative annotation of chromatin elements from ENCODE data. (In Review)

Genome Segmentation

• ChromHMM
  Learns and characterizes chromatin states.

  Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and characterization. Nat Methods. 2012 Feb 28;9(3):215-6.

• Segtools
  A Python package that analyzes genomic segmentations. The software efficiently calculates a variety of summary statistics and produces corresponding publication quality visualizations. The overall goal of Segtools is to provide a bird's-eye view of complex genomic data sets, allowing researchers to easily generate and confirm hypotheses.

  Buske OJ, Hoffman MM, Ponts N, Le Roch KG, Noble WS. Exploratory analysis of genomic segmentations with Segtools. BMC Bioinformatics. 2011 Oct 26;12:415.

• Segway
  Uses a machine learning method to analyze multiple tracks of functional genomics data, searching for recurring patterns. The software automatically partitions the genome into non-overlapping segments and assigns each segment a label. The resulting annotation provides a human-interpretable summary of the functional landscape of the genome, yielding hypotheses about novel instances or classes of functional elements.

  Hoffman MM, Buske OJ, Wang J, Weng Z, Bilmes JA, Noble WS. Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nat Methods. 2012 Mar 18;9(5):473-6.

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Other

• ACT
  Performs aggregation and correlation analyses of genomic tracks.

  Jee J, Rozowsky J, Yip KY, Lochovsky L, Bjornson R, Zhong G, Zhang Z, Fu Y, Wang J, Weng Z et al. ACT: aggregation and correlation toolbox for analyses of genome tracks. Bioinformatics. 2011 Apr 15;27(8):1152-4.

• CAGT (Clustering AGgregation Tool)
  Deciphers the heterogeneity and diversity of profiles of functional signals (e.g., chromatin mark ChIP-seq signal) centered at a collection of sites (e.g., TSSs or TF binding sites) in a genome. Rather than averaging the profiles over all the anchor sites (traditional aggregation plots), CAGT accounts for the inherent heterogeneity in signal magnitude, shape and orientation. CAGT partitions the set of anchor sites into compact clusters such that each cluster represents anchor points that show similar patterns of the functional signal profiles with different clusters having distinct patterns. The different groups of patterns are often enriched for distinct biological functions.

• TIP
  Predicts the targets of transcription factors using the data from ChIP-seq experiments.

  Cheng C, Min R, Gerstein M. TIP: a probabilistic method for identifying transcription factor target genes from ChIP-seq binding profiles. Bioinformatics. 2011 Dec 1;27(23):3221-7.

• NSC, RSC, PBC
  The NSC (Normalized strand cross-correlation) and RSC (relative strand cross-correlation) metrics use cross-correlation of stranded read density profiles to measure enrichment independently of peak calling. The PBC (PCR bottleneck coefficient) is an approximate measure of library complexity. PBC is the ratio of (non-redundant, uniquely mappable reads)/(uniquely mappable reads). The code for computing RSC and NSC can be downloaded from the phantompeakqualtools project page.

  Kundaje A, Jung LY, Kharchenko P, Wold B, Sidow A, Batzoglou S, Park P. Assessment of ChIP-seq data quality using cross-correlation analysis. (Submitted)

• SPOT (Signal Portion of Tags)
  Measures signal-to-noise in genome-wide epigenetic profiling assays by calculating the fraction of reads that fall in tag-enriched regions (see the Hotspot program, described above) from a sample of 10 million reads. The SPOT methodology can be generalized to use any peak-finder. A publication of SPOT and a more complete description are in preparation.

• BEDOPS
  Performs common genomic analysis tasks and offers improved flexibility, scalability and execution time characteristics over previously published packages. The suite includes a utility to compress large inputs into a lossless format that can provide greater space savings and faster data extractions than alternatives.

  Neph S, Kuehn MS, Reynolds AP, Haugen E, Thurman RE, Johnson AK, Rynes E, Maurano MT, Vierstra J, Thomas S et al. BEDOPS: high-performance genomic feature operations. Bioinformatics. 2012 Jul 15;28(14):1919-20.

• Genomedata
  Efficiently stores multiple tracks of numeric data anchored to a genome. The format allows fast random access to hundreds of gigabytes of data, while retaining a small disk space footprint. Utilities have also been developed to load data into this format. A reference implementation in Python and C components is available under the GNU General Public License.

  Hoffman MM, Buske OJ, Noble WS. The Genomedata format for storing large-scale functional genomics data. Bioinformatics. 2010 Jun 1;26(11):1458-9.