Vom Huhn abgeleitete Antikörper für Diagnostik und Immuntherapie

Due to the large evolutionary distance between birds (Aves) und humans, immunization of chickens with human proteins results in a strong response of the bird’s adaptive immune system to proteins of mammalian origin. Additionally, chicken-derived antibodies display less undesired cross-reactivity in analytical setups than conventional rodent-derived antibodies. Due to these features as well as the facile amplification of antibody-coding genes, chicken-derived antibodies emerged as promising molecules for the immunotherapy and various biotechnological applications.

ExOrthist: a tool to infer exon orthologies at any evolutionary distance

Several bioinformatic tools have been developed for genome-wide identification of orthologous and paralogous genes. However, no corresponding tool allows the detection of exon homology relationships. Here, we present ExOrthist, a fully reproducible Nextflow-based software enabling inference of exon homologs and orthogroups, visualization of evolution of exon-intron structures, and assessment of conservation of alternative splicing patterns. ExOrthist evaluates exon sequence conservation and considers the surrounding exon-intron context to derive genome-wide multi-species exon homologies at any evolutionary distance. We demonstrate its use in different evolutionary scenarios: whole genome duplication in frogs and convergence of Nova-regulated splicing networks (https://github.com/biocorecrg/ExOrthist).

Approximation Algorithms for Sorting λ-Permutations by λ-Operations

Understanding how different two organisms are is one question addressed by the comparative genomics field. A well-accepted way to estimate the evolutionary distance between genomes of two organisms is finding the rearrangement distance, which is the smallest number of rearrangements needed to transform one genome into another. By representing genomes as permutations, one of them can be represented as the identity permutation, and, so, we reduce the problem of transforming one permutation into another to the problem of sorting a permutation using the minimum number of rearrangements. This work investigates the problems of sorting permutations using reversals and/or transpositions, with some additional restrictions of biological relevance. Given a value λ, the problem now is how to sort a λ-permutation, which is a permutation whose elements are less than λ positions away from their correct places (regarding the identity), by applying the minimum number of rearrangements. Each λ-rearrangement must have size, at most, λ, and, when applied to a λ-permutation, the result should also be a λ-permutation. We present algorithms with approximation factors of O(λ2), O(λ), and O(1) for the problems of Sorting λ-Permutations by λ-Reversals, by λ-Transpositions, and by both operations.

Comprehensive machine-learning-based analysis of microRNA–target interactions reveals variable transferability of interaction rules across species

Background MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally via base-pairing with complementary sequences on messenger RNAs (mRNAs). Due to the technical challenges involved in the application of high-throughput experimental methods, datasets of direct bona fide miRNA targets exist only for a few model organisms. Machine learning (ML)-based target prediction models were successfully trained and tested on some of these datasets. There is a need to further apply the trained models to organisms in which experimental training data are unavailable. However, it is largely unknown how the features of miRNA–target interactions evolve and whether some features have remained fixed during evolution, raising questions regarding the general, cross-species applicability of currently available ML methods. Results We examined the evolution of miRNA–target interaction rules and used data science and ML approaches to investigate whether these rules are transferable between species. We analyzed eight datasets of direct miRNA–target interactions in four species (human, mouse, worm, cattle). Using ML classifiers, we achieved high accuracy for intra-dataset classification and found that the most influential features of all datasets overlap significantly. To explore the relationships between datasets, we measured the divergence of their miRNA seed sequences and evaluated the performance of cross-dataset classification. We found that both measures coincide with the evolutionary distance between the compared species. Conclusions The transferability of miRNA–targeting rules between species depends on several factors, the most associated factors being the composition of seed families and evolutionary distance. Furthermore, our feature-importance results suggest that some miRNA–target features have evolved while others remained fixed during the evolution of the species. Our findings lay the foundation for the future development of target prediction tools that could be applied to “non-model” organisms for which minimal experimental data are available. Availability and implementation The code is freely available at https://github.com/gbenor/TPVOD.

Comprehensive machine-learning-based analysis of microRNA-target interactions reveals variable transferability of interaction rules across species

BackgroundMicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally via base-pairing with complementary sequences on messenger RNAs (mRNAs). Due to the technical challenges involved in the application of high-throughput experimental methods, datasets of direct bona-fide miRNA targets exist only for a few model organisms. Machine learning (ML) based target prediction methods were successfully trained and tested on some of these datasets. There is a need to further apply the trained models to organisms where experimental training data is unavailable. However, it is largely unknown how the features of miRNA-target interactions evolve and whether there are features that have been fixed during evolution, questioning the general applicability of these ML methods across species.ResultsIn this paper, we examined the evolution of miRNA-target interaction rules and used data science and ML approaches to investigate whether these rules are transferable between species. We analyzed eight datasets of direct miRNA-target interactions in four organisms (human, mouse, worm, cattle). Using ML classifiers, we achieved high accuracy for intra-dataset classification and found that the most influential features of all datasets significantly overlap. To explore the relationships between datasets we measured the divergence of their miRNA seed sequences and evaluated the performance of cross-datasets classification. We showed that both measures coincide with the evolutionary distance of the compared organisms.ConclusionsOur results indicate that the transferability of miRNA-targeting rules between organisms depends on several factors, the most associated factors being the composition of seed families and evolutionary distance. Furthermore, our feature importance results suggest that some miRNA-target features have been evolving while some have been fixed during evolution. Our study lays the foundation for the future developments of target prediction tools that could be applied to “non-model” organisms for which minimal experimental data is available.Availability and implementation The code is freely available at https://github.com/gbenor/TPVOD

ExOrthist: a tool to infer exon orthologies at any evolutionary distance

Several bioinformatic tools have been developed for genome-wide identification of orthologous and paralogous genes among species. However, no existing tool allows the detection of orthologous/paralogous exons. Here, we present ExOrthist, a fully reproducible Nextflow-based software enabling to (i) infer exon homologs and orthogroups, (ii) visualize evolution of exon-intron structures, and (iii) assess conservation of alternative splicing patterns. ExOrthist not only evaluates exon sequence conservation but also considers the surrounding exon-intron context to derive genome-wide multi-species exon homologies at any evolutionary distance. We demonstrate its use in various evolutionary scenarios, from whole genome duplication to convergence of alternative splicing networks.

Reinforcement learning based metric filtering for evolutionary distance metric learning

Data collection plays an important role in business agility; data can prove valuable and provide insights for important features. However, conventional data collection methods can be costly and time-consuming. This paper proposes a hybrid system R-EDML that combines a sequential feature selection performed by Reinforcement Learning (RL) with the evolutionary feature prioritization of Evolutionary Distance Metric Learning (EDML) in a clustering process. The goal is to reduce the features while maintaining or increasing the accuracy leading to less time complexity and future data collection time and cost reduction. In this method, features represented by the diagonal elements of EDML matrices are prioritized using a differential evolution algorithm. Further, a selection control strategy using RL is learned by sequentially inserting and evaluating the prioritized elements. The outcome offers the best accuracy R-EDML matrix with the least number of elements. Diagonal R-EDML focusing on the diagonal elements is compared with EDML and conventional feature selection. Full Matrix R-EDML focusing on the diagonal and non-diagonal elements is tested and compared with Information-Theoretic Metric Learning. Moreover, R-EDML policy is tested for each EDML generation and across all generations. Results show a significant decrease in the number of features while maintaining or increasing accuracy.

Chromosome End Diversification in Sciarid Flies

Background: Dipterans exhibit a remarkable diversity of chromosome end structures in contrast to the conserved system defined by telomerase and short repeats. Within dipteran families, structure of chromosome termini is usually conserved within genera. With the aim to assess whether or not the evolutionary distance between genera implies chromosome end diversification, this report exploits two representatives of Sciaridae, Rhynchosciara americana, and Trichomegalosphys pubescens. Methods: Probes and plasmid microlibraries obtained by chromosome end microdissection, in situ hybridization, cloning, and sequencing are among the methodological approaches employed in this work. Results: The data argue for the existence of either specific terminal DNA sequences for each chromosome tip in T. pubescens, or sequences common to all chromosome ends but their extension does not allow detection by in situ hybridization. Both sciarid species share terminal sequences that are significantly underrepresented in chromosome ends of T. pubescens. Conclusions: The data suggest an unusual terminal structure in T. pubescens chromosomes compared to other dipterans investigated. A putative, evolutionary process of repetitive DNA expansion that acted differentially to shape chromosome ends of the two flies is also discussed.

On the Complexity of Some Variations of Sorting by Transpositions

One of the main challenges in Computational Biology is to find the evolutionary distance between two organisms. In the field of comparative genomics, one way to estimate such distance is to find a minimum cost sequence of rearrangements (large scale mutations) needed to transform one genome into another, which is called the rearrangement distance. In the past decades, these problems were studied considering many types of rearrangements (such as reversals, transpositions, transreversals, and revrevs) and considering the same weight for all rearrangements, or different weights depending on the types of rearrangements. The complexity of the problems involving reversals, transpositions, and both rearrangements is known, even though the hardness proof for the problem combining reversals and transpositions was recently given. In this paper, we enhance the knowledge for these problems by proving that models involving transpositions alongside reversals, transreversals, and revrevs are NP-hard, considering weights w1 for reversals and w2 for the other rearrangements such that w2/w1 ≤ 1.5. In addition, we address a cost function related to the number of fragmentations caused by a rearrangement, proving that the problem of finding a minimum cost sorting sequence, considering the fragmentation cost function with some restrictions, is NP-hard for transpositions and the combination of reversals and transpositions.