An increasing number of genomic tracks such as DNA methylation, histone modifications or transcriptomes are being produced to annotate genomes with functional states. The comparison of such high dimensional vectors obtained under various experimental conditions requires the use of a distance or dissimilarity measure. Pearson, Cosine and $L_{p}$-norm distances are commonly used for both count and binary vectors. In this article, we highlight how enhancement methods such as the contrast increasing mutual proximity' (MP) or local scaling' improve common distance measures. We present a systematic approach to evaluate the performance of such enhanced distance measures in terms of separability of groups of experimental replicates to outline their effect. We show that the MP' applied on the various distance measures drastically increases performance. Depending on the type of epigenetic experiment, MP' coupled together with Pearson, Cosine, $L_1$, Yule or Jaccard distances proves to be highly efficient in discriminating epigenomic profiles.

SUMMARY: Genomic sequences are widely used to infer the evolutionary history of a given group of individuals. Many methods have been developed for sequence clustering and tree building. In the early days of genome sequencing, these were often limited to hundreds of sequences but due to the surge of high throughput sequencing, it is now common to have millions of sampled sequences at hand. We introduce MNHN-Tree-Tools, a high performance set of algorithms that builds multi-scale, nested clusters of sequences found in a FASTA file. MNHN-Tree-Tools does not rely on multiple sequence alignment and can thus be used on large datasets to infer a sequence tree. Herein, we outline two applications: a human alpha-satellite repeats classification and a tree of life derivation from 16S/18S rDNA sequences. AVAILABILITY AND IMPLEMENTATION: Open source with a Zlib License via the Git protocol: https://gitlab.in2p3.fr/mnhn-tools/mnhn-tree-tools. MANUAL: A detailed users guide and tutorial: https://gitlab.in2p3.fr/mnhn-tools/mnhn-tree-tools-manual/-/raw/master/manual.pdf. WEBSITE AND FAQ: http://treetools.haschka.net. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Prediction of genomic annotations from DNA sequences using deep learning is today becoming a flourishing field with many applications. Nevertheless, there are still difficulties in handling data in order to conveniently build and train models dedicated for specific end-user's tasks. keras_dna is designed for an easy implementation of Keras models (TensorFlow high level API) for genomics. It can handle standard bioinformatic files formats as inputs such as bigwig, gff, bed, wig, bedGraph or fasta and returns standardized inputs for model training. keras_dna is designed to implement existing models but also to facilitate the development of news models that can have single or multiple targets or inputs. AVAILABILITY AND IMPLEMENTATION: Freely available with a MIT License using pip install keras_dna or cloning the github repo at https://github.com/etirouthier/keras_dna.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Genome-wide chromosomal contact maps are widely used to uncover the 3D organization of genomes. They rely on collecting millions of contacting pairs of genomic loci. Contacts at short range are usually well measured in experiments, while there is a lot of missing information about long-range contacts. RESULTS: We propose to use the sparse information contained in raw contact maps to infer high-confidence contact counts between all pairs of loci. Our algorithmic procedure, Boost-HiC, enables the detection of Hi-C patterns such as chromosomal compartments at a resolution that would be otherwise only attainable by sequencing a hundred times deeper the experimental Hi-C library. Boost-HiC can also be used to compare contact maps at an improved resolution. AVAILABILITY AND IMPLEMENTATION: Boost-HiC is available at https://github.com/LeopoldC/Boost-HiC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.