New computational biology tools for the systematic analysis of the structure and expression of human genes

From the late 1980s, the automation of sequencing techniques and the computer spread gave rise to a flourishing number of new molecular structures and sequences and to proliferation of new databases in which to store them. Here are presented three computational approaches able to analyse the massive amount of publicly avalilable data in order to answer to important biological questions. The first strategy studies the incorrect assignment of the first AUG codon in a messenger RNA (mRNA), due to the incomplete determination of its 5' end sequence. An extension of the mRNA 5' coding region was identified in 477 in human loci, out of all human known mRNAs analysed, using an automated expressed sequence tag (EST)-based approach. Proof-of-concept confirmation was obtained by in vitro cloning and sequencing for GNB2L1, QARS and TDP2 and the consequences for the functional studies are discussed. The second approach analyses the codon bias, the phenomenon in which distinct synonymous codons are used with different frequencies, and, following integration with a gene expression profile, estimates the total number of codons present across all the expressed mRNAs (named here "codonome value") in a given biological condition. Systematic analyses across different pathological and normal human tissues and multiple species shows a surprisingly tight correlation between the codon bias and the codonome bias. The third approach is useful to studies the expression of human autism spectrum disorder (ASD) implicated genes. ASD implicated genes sharing microRNA response elements (MREs) for the same microRNA are co-expressed in brain samples from healthy and ASD affected individuals. The different expression of a recently identified long non coding RNA which have four MREs for the same microRNA could disrupt the equilibrium in this network, but further analyses and experiments are needed.

Bioconductor: Open software development for computational biology and bioinformatics

The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. We detail some of the design decisions, software paradigms and operational strategies that have allowed a small number of researchers to provide a wide variety of innovative, extensible, software solutions in a relatively short time. The use of an object oriented programming paradigm, the adoption and development of a software package system, designing by contract, distributed development and collaboration with other projects are elements of this project's success. Individually, each of these concepts are useful and important but when combined they have provided a strong basis for rapid development and deployment of innovative and flexible research software for scientific computation. A primary objective of this initiative is achievement of total remote reproducibility of novel algorithmic research results.

Quantifying Reproducibility in Computational Biology: The Case of the Tuberculosis Drugome

How easy is it to reproduce the results found in a typical computational biology paper? Either through experience or intuition the reader will already know that the answer is with difficulty or not at all. In this paper we attempt to quantify this difficulty by reproducing a previously published paper for different classes of users (ranging from users with little expertise to domain experts) and suggest ways in which the situation might be improved. Quantification is achieved by estimating the time required to reproduce each of the steps in the method described in the original paper and make them part of an explicit workflow that reproduces the original results. Reproducing the method took several months of effort, and required using new versions and new software that posed challenges to reconstructing and validating the results. The quantification leads to “reproducibility maps” that reveal that novice researchers would only be able to reproduce a few of the steps in the method, and that only expert researchers with advance knowledge of the domain would be able to reproduce the method in its entirety. The workflow itself is published as an online resource together with supporting software and data. The paper concludes with a brief discussion of the complexities of requiring reproducibility in terms of cost versus benefit, and a desiderata with our observations and guidelines for improving reproducibility. This has implications not only in reproducing the work of others from published papers, but reproducing work from one’s own laboratory.

Deep learning and embeddings for problems of computational biology

The development of Next Generation Sequencing promotes Biology in the Big Data era. The ever-increasing gap between proteins with known sequences and those with a complete functional annotation requires computational methods for automatic structure and functional annotation. My research has been focusing on proteins and led so far to the development of three novel tools, DeepREx, E-SNPs&GO and ISPRED-SEQ, based on Machine and Deep Learning approaches. DeepREx computes the solvent exposure of residues in a protein chain. This problem is relevant for the definition of structural constraints regarding the possible folding of the protein. DeepREx exploits Long Short-Term Memory layers to capture residue-level interactions between positions distant in the sequence, achieving state-of-the-art performances. With DeepRex, I conducted a large-scale analysis investigating the relationship between solvent exposure of a residue and its probability to be pathogenic upon mutation. E-SNPs&GO predicts the pathogenicity of a Single Residue Variation. Variations occurring on a protein sequence can have different effects, possibly leading to the onset of diseases. E-SNPs&GO exploits protein embeddings generated by two novel Protein Language Models (PLMs), as well as a new way of representing functional information coming from the Gene Ontology. The method achieves state-of-the-art performances and is extremely time-efficient when compared to traditional approaches. ISPRED-SEQ predicts the presence of Protein-Protein Interaction sites in a protein sequence. Knowing how a protein interacts with other molecules is crucial for accurate functional characterization. ISPRED-SEQ exploits a convolutional layer to parse local context after embedding the protein sequence with two novel PLMs, greatly surpassing the current state-of-the-art. All methods are published in international journals and are available as user-friendly web servers. They have been developed keeping in mind standard guidelines for FAIRness (FAIR: Findable, Accessible, Interoperable, Reusable) and are integrated into the public collection of tools provided by ELIXIR, the European infrastructure for Bioinformatics.

Integration and visualization of systems biology data in context of the genome

Background: High-density tiling arrays and new sequencing technologies are generating rapidly increasing volumes of transcriptome and protein-DNA interaction data. Visualization and exploration of this data is critical to understanding the regulatory logic encoded in the genome by which the cell dynamically affects its physiology and interacts with its environment. Results: The Gaggle Genome Browser is a cross-platform desktop program for interactively visualizing high-throughput data in the context of the genome. Important features include dynamic panning and zooming, keyword search and open interoperability through the Gaggle framework. Users may bookmark locations on the genome with descriptive annotations and share these bookmarks with other users. The program handles large sets of user-generated data using an in-process database and leverages the facilities of SQL and the R environment for importing and manipulating data. A key aspect of the Gaggle Genome Browser is interoperability. By connecting to the Gaggle framework, the genome browser joins a suite of interconnected bioinformatics tools for analysis and visualization with connectivity to major public repositories of sequences, interactions and pathways. To this flexible environment for exploring and combining data, the Gaggle Genome Browser adds the ability to visualize diverse types of data in relation to its coordinates on the genome. Conclusions: Genomic coordinates function as a common key by which disparate biological data types can be related to one another. In the Gaggle Genome Browser, heterogeneous data are joined by their location on the genome to create information-rich visualizations yielding insight into genome organization, transcription and its regulation and, ultimately, a better understanding of the mechanisms that enable the cell to dynamically respond to its environment.

Experimental and computational approaches to quantitative proteomics: status quo and outlook.

Proteomics has come a long way from the initial qualitative analysis of proteins present in a given sample at a given time ("cataloguing") to large-scale characterization of proteomes, their interactions and dynamic behavior. Originally enabled by breakthroughs in protein separation and visualization (by two-dimensional gels) and protein identification (by mass spectrometry), the discipline now encompasses a large body of protein and peptide separation, labeling, detection and sequencing tools supported by computational data processing. The decisive mass spectrometric developments and most recent instrumentation news are briefly mentioned accompanied by a short review of gel and chromatographic techniques for protein/peptide separation, depletion and enrichment. Special emphasis is placed on quantification techniques: gel-based, and label-free techniques are briefly discussed whereas stable-isotope coding and internal peptide standards are extensively reviewed. Another special chapter is dedicated to software and computing tools for proteomic data processing and validation. A short assessment of the status quo and recommendations for future developments round up this journey through quantitative proteomics.

Computational problems in perfect phylogeny haplotyping: typing without calling the allele.

A haplotype is an m-long binary vector. The XOR-genotype of two haplotypes is the m-vector of their coordinate-wise XOR. We study the following problem: Given a set of XOR-genotypes, reconstruct their haplotypes so that the set of resulting haplotypes can be mapped onto a perfect phylogeny (PP) tree. The question is motivated by studying population evolution in human genetics, and is a variant of the perfect phylogeny haplotyping problem that has received intensive attention recently. Unlike the latter problem, in which the input is "full" genotypes, here we assume less informative input, and so may be more economical to obtain experimentally. Building on ideas of Gusfield, we show how to solve the problem in polynomial time, by a reduction to the graph realization problem. The actual haplotypes are not uniquely determined by that tree they map onto, and the tree itself may or may not be unique. We show that tree uniqueness implies uniquely determined haplotypes, up to inherent degrees of freedom, and give a sufficient condition for the uniqueness. To actually determine the haplotypes given the tree, additional information is necessary. We show that two or three full genotypes suffice to reconstruct all the haplotypes, and present a linear algorithm for identifying those genotypes.

The impact of theoretical chemistry in Biology

Els avenços en les bases dels mètodes teòrics i l'espectacular desenvolupament de la potència de càlcul han fet possible progressar enormement en el somni dels fundadors de la química, és a dir, ser capaços d'estudiar amb mètodes computacionals el conjunt de processos químics. Actualment, la química teòrica està completant el darrer avenç: intentar esdevenir l'eina més recent per a comprendre la naturalesa química dels éssers vius. Aquesta revisió pretén mostrar com els mètodes de la química teòrica, originalment desenvolupats per a examinar molècules petites en fase gas, han evolucionat per a assolir la complexa descripció de sistemes biològics.

Computational problems in perfect phylogeny haplotyping: Typing without calling the allele

A haplotype is an m-long binary vector. The XOR-genotype of two haplotypes is the m-vector of their coordinate-wise XOR. We study the following problem: Given a set of XOR-genotypes, reconstruct their haplotypes so that the set of resulting haplotypes can be mapped onto a perfect phylogeny (PP) tree. The question is motivated by studying population evolution in human genetics and is a variant of the PP haplotyping problem that has received intensive attention recently. Unlike the latter problem, in which the input is '' full '' genotypes, here, we assume less informative input and so may be more economical to obtain experimentally. Building on ideas of Gusfield, we show how to solve the problem in polynomial time by a reduction to the graph realization problem. The actual haplotypes are not uniquely determined by the tree they map onto and the tree itself may or may not be unique. We show that tree uniqueness implies uniquely determined haplotypes, up to inherent degrees of freedom, and give a sufficient condition for the uniqueness. To actually determine the haplotypes given the tree, additional information is necessary. We show that two or three full genotypes suffice to reconstruct all the haplotypes and present a linear algorithm for identifying those genotypes.

Computational prediction of neural progenitor cell fates

Understanding how stem and progenitor cells choose between alternative cell fates is a major challenge in developmental biology. Efforts to tackle this problem have been hampered by the scarcity of markers that can be used to predict cell division outcomes. Here we present a computational method, based on algorithmic information theory, to analyze dynamic features of living cells over time. Using this method, we asked whether rat retinal progenitor cells (RPCs) display characteristic phenotypes before undergoing mitosis that could foretell their fate. We predicted whether RPCs will undergo a self-renewing or terminal division with 99% accuracy, or whether they will produce two photoreceptors or another combination of offspring with 87% accuracy. Our implementation can segment, track and generate predictions for 40 cells simultaneously on a standard computer at 5 min per frame. This method could be used to isolate cell populations with specific developmental potential, enabling previously impossible investigations.

Regulation of early steps of GPVI signal transduction by phosphatases: a systems biology approach

We present a data-driven mathematical model of a key initiating step in platelet activation, a central process in the prevention of bleeding following Injury. In vascular disease, this process is activated inappropriately and causes thrombosis, heart attacks and stroke. The collagen receptor GPVI is the primary trigger for platelet activation at sites of injury. Understanding the complex molecular mechanisms initiated by this receptor is important for development of more effective antithrombotic medicines. In this work we developed a series of nonlinear ordinary differential equation models that are direct representations of biological hypotheses surrounding the initial steps in GPVI-stimulated signal transduction. At each stage model simulations were compared to our own quantitative, high-temporal experimental data that guides further experimental design, data collection and model refinement. Much is known about the linear forward reactions within platelet signalling pathways but knowledge of the roles of putative reverse reactions are poorly understood. An initial model, that includes a simple constitutively active phosphatase, was unable to explain experimental data. Model revisions, incorporating a complex pathway of interactions (and specifically the phosphatase TULA-2), provided a good description of the experimental data both based on observations of phosphorylation in samples from one donor and in those of a wider population. Our model was used to investigate the levels of proteins involved in regulating the pathway and the effect of low GPVI levels that have been associated with disease. Results indicate a clear separation in healthy and GPVI deficient states in respect of the signalling cascade dynamics associated with Syk tyrosine phosphorylation and activation. Our approach reveals the central importance of this negative feedback pathway that results in the temporal regulation of a specific class of protein tyrosine phosphatases in controlling the rate, and therefore extent, of GPVI-stimulated platelet activation.

Symmetry in biology: from genetic code to stochastic gene regulation

Mathematical models, as instruments for understanding the workings of nature, are a traditional tool of physics, but they also play an ever increasing role in biology - in the description of fundamental processes as well as that of complex systems. In this review, the authors discuss two examples of the application of group theoretical methods, which constitute the mathematical discipline for a quantitative description of the idea of symmetry, to genetics. The first one appears, in the form of a pseudo-orthogonal (Lorentz like) symmetry, in the stochastic modelling of what may be regarded as the simplest possible example of a genetic network and, hopefully, a building block for more complicated ones: a single self-interacting or externally regulated gene with only two possible states: ` on` and ` off`. The second is the algebraic approach to the evolution of the genetic code, according to which the current code results from a dynamical symmetry breaking process, starting out from an initial state of complete symmetry and ending in the presently observed final state of low symmetry. In both cases, symmetry plays a decisive role: in the first, it is a characteristic feature of the dynamics of the gene switch and its decay to equilibrium, whereas in the second, it provides the guidelines for the evolution of the coding rules.