MOTIVATION: Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard method to investigate chromatin protein composition. As the number of community-available ChIP-seq profiles increases, it becomes more common to use data from different sources, which makes joint analysis challenging. Issues such as lack of reproducibility, heterogeneous quality and conflicts between replicates become evident when comparing datasets, especially when they are produced by different laboratories. RESULTS: Here, we present Zerone, a ChIP-seq discretizer with built-in quality control. Zerone is powered by a Hidden Markov Model with zero-inflated negative multinomial emissions, which allows it to merge several replicates into a single discretized profile. To identify low quality or irreproducible data, we trained a Support Vector Machine and integrated it as part of the discretization process. The result is a classifier reaching 95% accuracy in detecting low quality profiles. We also introduce a graphical representation to compare discretization quality and we show that Zerone achieves outstanding accuracy. Finally, on current hardware, Zerone discretizes a ChIP-seq experiment on mammalian genomes in about 5 min using less than 700 MB of memory. AVAILABILITY AND IMPLEMENTATION: Zerone is available as a command line tool and as an R package. The C source code and R scripts can be downloaded from https://github.com/nanakiksc/zerone The information to reproduce the benchmark and the figures is stored in a public Docker image that can be downloaded from https://hub.docker.com/r/nanakiksc/zerone/ CONTACT: : [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The increasing throughput of sequencing technologies offers new applications and challenges for computational biology. In many of those applications, sequencing errors need to be corrected. This is particularly important when sequencing reads from an unknown reference such as random DNA barcodes. In this case, error correction can be done by performing a pairwise comparison of all the barcodes, which is a computationally complex problem. RESULTS: Here, we address this challenge and describe an exact algorithm to determine which pairs of sequences lie within a given Levenshtein distance. For error correction or redundancy reduction purposes, matched pairs are then merged into clusters of similar sequences. The efficiency of starcode is attributable to the poucet search, a novel implementation of the Needleman-Wunsch algorithm performed on the nodes of a trie. On the task of matching random barcodes, starcode outperforms sequence clustering algorithms in both speed and precision. AVAILABILITY AND IMPLEMENTATION: The C source code is available at http://github.com/gui11aume/starcode.