Biology subjects

Sanches, D. S.

Publications and source records attributed to Sanches, D. S..

5 recordsLinked to original sources

bioRxiv2023

biomapp::chip: Large-Scale Motif Analysis

BackgroundDiscovery biological motifs plays a fundamental role in understanding regulatory mechanisms. Computationally, they can be efficiently represented as kmers, making the counting of these elEMents a critical aspect for ensuring not only the accuracy but also the efficiency of the analytical process. This is particularly useful in scenarios involving large data volumes, such as those generated by the ChIP-seq protocol. Against this backdrop, we introduce O_SCPLOWBIOMAPPC_SCPLOWO_SCPCAP ::C_SCPCAPO_SCPLOWCHIPC_SCPLOW, a tool specifically designed to optimize the discovery of biological motifs in large data volumes. ResultsWe conducted a comprehensive set of comparative tests with state-of-the-art algorithms. Our analyses revealed that O_SCPLOWBIOMAPPC_SCPLOWO_SCPCAP ::C_SCPCAPO_SCPLOWCHIPC_SCPLOW outperforms existing approaches in various metrics, excelling both in terms of performance and accuracy. The tests demonstrated a higher detection rate of significant motifs and also greater agility in the execution of the algorithm. Furthermore, the O_SCPLOWSMTC_SCPLOW component played a vital role in the systems efficiency, proving to be both agile and accurate in kmer counting, which in turn improved the overall efficacy of our tool. ConclusionO_SCPLOWBIOMAPPC_SCPLOWO_SCPCAP ::C_SCPCAPO_SCPLOWCHIPC_SCPLOW represent real advancements in the discovery of biological motifs, particularly in large data volume scenarios, offering a relevant alternative for the analysis of ChIP-seq data and have the potential to boost future research in the field. This software can be found at the following address: https://github.com/jadermcg/BIOMAPP-CHIP.

bioinformatics↗

bioRxiv2023

Simulation of 69 microbial communities indicates sequencing depth and false positives are major drivers of bias in Prokaryotic metagenome-assembled genome recovery

We hypothesize that sample evenness, sequencing depth and taxonomic relatedness influence the recovery of metagenome-assembled genomes (MAGs). To test this hypothesis, we assessed MAG recovery in three in silico microbial communities composed of 42 species with the same richness but different sample evenness, sequencing depth and taxonomic distribution profiles using three different pipelines for MAG recovery. The pipeline developed by Parks and colleagues (8K) generated the highest number of MAGs and the lowest number of true positives per community profile. The pipeline by Karst and colleagues (DT) showed the most accurate results ([~] 92%), outperforming the 8K and Multi-Metagenome pipeline (MM) developed by Albertsen and collaborators. Sequencing depth influenced the accurate recovery of genomes when using the 8K and MM, even with contrasting patterns: the MM pipeline recovered more MAGs found in the original communities when employing sequencing depths up to 60 million reads, whilst the 8K recovered more true positives in communities sequenced above 60 million reads. DT showed the best species recovery from the same genus, even though close-related species have a low recovery rate in all pipelines. Our results highlight that more bins do not translate to the actual community composition and that sequencing depth plays a role in MAG recovery and increased community resolution. Even low MAG recovery error rates can significantly impact biological inferences. Our data indicates the scientific community should their findings from MAG recovery, especially when asserting novel species or metabolic traits.

bioinformatics↗

bioRxiv2023

Discovering motifs and genomic patterns with SMT: a high-performance data structure for counting kmers

MotivationThe search for conserved motifs in DNA sequences is an important problem in bioinformatics. The growing availability of large-scale genomic data poses significant challenges for computational biology, particularly in terms of efficiency in analysis, kmer identification, and noise presence. The detection of conserved motifs and patterns in DNA sequences is crucial for understanding gene functions and regulations. Therefore, it is essential to develop a data structure that can handle these large volumes of information and provide accurate and fast results. ResultsWe present SMT, an innovative tool designed to efficiently store and count kmers, optimizing memory usage and computation time. The application of SMT has also proven effective in discovering motifs in noisy datasets, allowing the identification of conserved regions in sequences. Furthermore, SMT enables exact searches in constant time and recovers the most abundant k-mers, as well as performs approximate searches in linear time to find fragments with up to d mutations. This approach facilitates large-scale data analysis and provides important insights into the conserved properties of biological sequences. The application of SMT in motif discovery demonstrates its potential to drive research in bioinformatics and genomics. Supplementary data and results are available to provide additional information and support the conclusions presented in this work. Availability and implementationThe source code of the presented method is publicly available at https://github.com/jadermcg/SMT.

bioinformatics↗

bioRxiv2020

MathFeature: Feature Extraction Package for Biological Sequences Based on Mathematical Descriptors

Machine learning algorithms have been very successfully applied to extract new and relevant knowledge from biological sequences. However, the predictive performance of these algorithms is largely affected by how the sequences are represented. Thereby, the main challenge is how to numerically represent a biological sequence in a numeric vector with an efficient mathematical expression. Several feature extraction techniques have been proposed for biological sequences, where most of them are available in feature extraction packages. However, there are relevant approaches that are not available in existing packages, techniques based on mathematical descriptors, e.g., Fourier, entropy, and graphs. Therefore, this paper presents a new package, named MathFeature, which implements mathematical descriptors able to extract relevant information from biological sequences. MathFeature provides 20 approaches based on several studies found in the literature, e.g., multiple numeric mappings, genomic signal processing, chaos game theory, entropy, and complex networks. MathFeature also allows the extraction of alternative features, complementing the existing packages. Availability and implementationMathFeature is freely available at https://bonidia.github.io/MathFeature/ or https://github.com/Bonidia/MathFeature Contactbonidia@usp.br, rpbonidia@gmail.com

bioinformatics↗

bioRxiv2020

Feature Extraction Approaches for Biological Sequences: A Comparative Study of Mathematical Models

The number of available biological sequences has increased significantly in recent years due to various genomic sequencing projects, creating a huge volume of data. Consequently, new computational methods are needed to analyze and extract information from these sequences. Machine learning methods have shown broad applicability in computational biology and bioinformatics. The utilization of machine learning methods has helped to extract relevant information from various biological datasets. However, there are still several obstacles that motivate new algorithms and pipeline proposals, mainly involving feature extraction problems, in which extracting significant discriminatory information from a biological set is challenging. Considering this, our work proposes to study and analyze a feature extraction pipeline based on mathematical models (Numerical Mapping, Fourier, Entropy, and Complex Networks). As a case study, we analyze Long Non-Coding RNA sequences. Moreover, we divided this work into two studies, e.g., (I) we assessed our proposal with the most addressed problem in our review, e.g., lncRNA vs. mRNA; (II) we tested its generalization on different classification problems, e.g., circRNA vs. lncRNA. The experimental results demonstrated three main contributions: (1) An in-depth study of several mathematical models; (2) a new feature extraction pipeline and (3) its generalization and robustness for distinct biological sequence classification.

bioinformatics↗