Careful feature selection is key in classification of Alzheimer's disease patients based on whole-genome sequencing data.

Despite great increase of the amount of data from genome-wide association studies (GWAS) and whole-genome sequencing (WGS), the genetic background of a partially heritable Alzheimer's disease (AD) is not fully understood yet. Machine learning methods are expected to help researchers in the analysis of the large number of SNPs possibly associated with the disease onset. To date, a number of such approaches were applied to genotype-based classification of AD patients and healthy controls using GWAS data and reported accuracy of 0.65-0.975. However, since the estimated influence of genotype on sporadic AD occurrence is lower than that, these very high classification accuracies may potentially be a result of overfitting. We have explored the possibilities of applying feature selection and classification using random forests to WGS and GWAS data from two datasets. Our results suggest that this approach is prone to overfitting if feature selection is performed before division of data into the training and testing set. Therefore, we recommend avoiding selection of features used to build the model based on data included in the testing set. We suggest that for currently available dataset sizes the expected classifier performance is between 0.55 and 0.7 (AUC) and higher accuracies reported in literature are likely a result of overfitting.

Mapping chromatin accessibility and active regulatory elements reveals pathological mechanisms in human gliomas.

Chromatin structure and accessibility, and combinatorial binding of transcription factors to regulatory elements in genomic DNA control transcription. Genetic variations in genes encoding histones, epigenetics-related enzymes or modifiers affect chromatin structure/dynamics and result in alterations in gene expression contributing to cancer development or progression. Gliomas are brain tumors frequently associated with epigenetics-related gene deregulation. We perform whole-genome mapping of chromatin accessibility, histone modifications, DNA methylation patterns and transcriptome analysis simultaneously in multiple tumor samples to unravel epigenetic dysfunctions driving gliomagenesis. Based on the results of the integrative analysis of the acquired profiles, we create an atlas of active enhancers and promoters in benign and malignant gliomas. We explore these elements and intersect with Hi-C data to uncover molecular mechanisms instructing gene expression in gliomas.

Petri net-based model of the human DNA base excision repair pathway.

Cellular DNA is daily exposed to several damaging agents causing a plethora of DNA lesions. As a first aid to restore DNA integrity, several enzymes got specialized in damage recognition and lesion removal during the process called base excision repair (BER). A large number of DNA damage types and several different readers of nucleic acids lesions during BER pathway as well as two sub-pathways were considered in the definition of a model using the Petri net framework. The intuitive graphical representation in combination with precise mathematical analysis methods are the strong advantages of the Petri net-based representation of biological processes and make Petri nets a promising approach for modeling and analysis of human BER. The reported results provide new information that will aid efforts to characterize in silico knockouts as well as help to predict the sensitivity of the cell with inactivated repair proteins to different types of DNA damage. The results can also help in identifying the by-passing pathways that may lead to lack of pronounced phenotypes associated with mutations in some of the proteins. This knowledge is very useful when DNA damage-inducing drugs are introduced for cancer therapy, and lack of DNA repair is desirable for tumor cell death.

MODOMICS: a database of RNA modification pathways. 2017 update.

MODOMICS is a database of RNA modifications that provides comprehensive information concerning the chemical structures of modified ribonucleosides, their biosynthetic pathways, the location of modified residues in RNA sequences, and RNA-modifying enzymes. In the current database version, we included the following new features and data: extended mass spectrometry and liquid chromatography data for modified nucleosides; links between human tRNA sequences and MINTbase - a framework for the interactive exploration of mitochondrial and nuclear tRNA fragments; new, machine-friendly system of unified abbreviations for modified nucleoside names; sets of modified tRNA sequences for two bacterial species, updated collection of mammalian tRNA modifications, 19 newly identified modified ribonucleosides and 66 functionally characterized proteins involved in RNA modification. Data from MODOMICS have been linked to the RNAcentral database of RNA sequences. MODOMICS is available at http://modomics.genesilico.pl.

RNAcentral: a comprehensive database of non-coding RNA sequences.

RNAcentral is a database of non-coding RNA (ncRNA) sequences that aggregates data from specialised ncRNA resources and provides a single entry point for accessing ncRNA sequences of all ncRNA types from all organisms. Since its launch in 2014, RNAcentral has integrated twelve new resources, taking the total number of collaborating database to 22, and began importing new types of data, such as modified nucleotides from MODOMICS and PDB. We created new species-specific identifiers that refer to unique RNA sequences within a context of single species. The website has been subject to continuous improvements focusing on text and sequence similarity searches as well as genome browsing functionality. All RNAcentral data is provided for free and is available for browsing, bulk downloads, and programmatic access at http://rnacentral.org/.

tRNAmodpred: A computational method for predicting posttranscriptional modifications in tRNAs.

tRNA molecules contain numerous chemically altered nucleosides, which are formed by enzymatic modification of the primary transcripts during the complex tRNA maturation process. Some of the modifications are introduced by single reactions, while other require complex series of reactions carried out by several different enzymes. The location and distribution of various types of modifications vary greatly between different tRNA molecules, organisms and organelles. We have developed a computational method tRNAmodpred, for predicting modifications in tRNA sequences. Briefly, our method takes as an input one or more unmodified tRNA sequences and a set of protein sequences corresponding to a proteome of a cell. Subsequently it identifies homologs of known tRNA modification enzymes in the proteome, predicts tRNA modification activities and maps them onto known pathways of RNA modification from the MODOMICS database. Thereby, theoretically possible modification pathways are identified, and products of these modification reactions are proposed for query tRNAs. This method allows for predicting modification patterns for newly sequenced genomes as well as for checking tentative modification status of tRNAs from one species treated with enzymes from another source, e.g. to predict the possible modifications of eukaryotic tRNAs expressed in bacteria. tRNAmodpred is freely available as a web server at http://genesilico.pl/trnamodpred/.

Phylogenomics and sequence-structure-function relationships in the GmrSD family of Type IV restriction enzymes.

BACKGROUND: GmrSD is a modification-dependent restriction endonuclease that specifically targets and cleaves glucosylated hydroxymethylcytosine (glc-HMC) modified DNA. It is encoded either as two separate single-domain GmrS and GmrD proteins or as a single protein carrying both domains. Previous studies suggested that GmrS acts as endonuclease and NTPase whereas GmrD binds DNA. METHODS: In this work we applied homology detection, sequence conservation analysis, fold recognition and homology modeling methods to study sequence-structure-function relationships in the GmrSD restriction endonucleases family. We also analyzed the phylogeny and genomic context of the family members. RESULTS: Results of our comparative genomics study show that GmrS exhibits similarity to proteins from the ParB/Srx fold which can have both NTPase and nuclease activity. In contrast to the previous studies though, we attribute the nuclease activity also to GmrD as we found it to contain the HNH endonuclease motif. We revealed residues potentially important for structure and function in both domains. Moreover, we found that GmrSD systems exist predominantly as a fused, double-domain form rather than as a heterodimer and that their homologs are often encoded in regions enriched in defense and gene mobility-related elements. Finally, phylogenetic reconstructions of GmrS and GmrD domains revealed that they coevolved and only few GmrSD systems appear to be assembled from distantly related GmrS and GmrD components. CONCLUSIONS: Our study provides insight into sequence-structure-function relationships in the yet poorly characterized family of Type IV restriction enzymes. Comparative genomics allowed to propose possible role of GmrD domain in the function of the GmrSD enzyme and possible active sites of both GmrS and GmrD domains. Presented results can guide further experimental characterization of these enzymes.

Distribution and frequencies of post-transcriptional modifications in tRNAs.

Functional tRNA molecules always contain a wide variety of post-transcriptionally modified nucleosides. These modifications stabilize tRNA structure, allow for proper interaction with other macromolecules and fine-tune the decoding of mRNAs during translation. Their presence in functionally important regions of tRNA is conserved in all domains of life. However, the identities of many of these modified residues depend much on the phylogeny of organisms the tRNAs are found in, attesting for domain-specific strategies of tRNA maturation. In this work we present a new tool, tRNAmodviz web server (http://genesilico.pl/trnamodviz) for easy comparative analysis and visualization of modification patterns in individual tRNAs, as well as in groups of selected tRNA sequences. We also present results of comparative analysis of tRNA sequences derived from 7 phylogenetically distinct groups of organisms: Gram-negative bacteria, Gram-positive bacteria, cytosol of eukaryotic single cell organisms, Fungi and Metazoa, cytosol of Viridiplantae, mitochondria, plastids and Euryarchaeota. These data update the study conducted 20 y ago with the tRNA sequences available at that time.

MODOMICS: a database of RNA modification pathways--2013 update.

MODOMICS is a database of RNA modifications that provides comprehensive information concerning the chemical structures of modified ribonucleosides, their biosynthetic pathways, RNA-modifying enzymes and location of modified residues in RNA sequences. In the current database version, accessible at http://modomics.genesilico.pl, we included new features: a census of human and yeast snoRNAs involved in RNA-guided RNA modification, a new section covering the 5'-end capping process, and a catalogue of 'building blocks' for chemical synthesis of a large variety of modified nucleosides. The MODOMICS collections of RNA modifications, RNA-modifying enzymes and modified RNAs have been also updated. A number of newly identified modified ribonucleosides and more than one hundred functionally and structurally characterized proteins from various organisms have been added. In the RNA sequences section, snRNAs and snoRNAs with experimentally mapped modified nucleosides have been added and the current collection of rRNA and tRNA sequences has been substantially enlarged. To facilitate literature searches, each record in MODOMICS has been cross-referenced to other databases and to selected key publications. New options for database searching and querying have been implemented, including a BLAST search of protein sequences and a PARALIGN search of the collected nucleic acid sequences.

RNApathwaysDB--a database of RNA maturation and decay pathways.

Many RNA molecules undergo complex maturation, involving e.g. excision from primary transcripts, removal of introns, post-transcriptional modification and polyadenylation. The level of mature, functional RNAs in the cell is controlled not only by the synthesis and maturation but also by degradation, which proceeds via many different routes. The systematization of data about RNA metabolic pathways and enzymes taking part in RNA maturation and degradation is essential for the full understanding of these processes. RNApathwaysDB, available online at http://iimcb.genesilico.pl/rnapathwaysdb, is an online resource about maturation and decay pathways involving RNA as the substrate. The current release presents information about reactions and enzymes that take part in the maturation and degradation of tRNA, rRNA and mRNA, and describes pathways in three model organisms: Escherichia coli, Saccharomyces cerevisiae and Homo sapiens. RNApathwaysDB can be queried with keywords, and sequences of protein enzymes involved in RNA processing can be searched with BLAST. Options for data presentation include pathway graphs and tables with enzymes and literature data. Structures of macromolecular complexes involving RNA and proteins that act on it are presented as 'potato models' using DrawBioPath-a new javascript tool.