Functional Annotation Tool Research Articles

Using In Silico Methods to Identify Protein Tyrosine Kinase A (PtkA) Homolog in Non-Tuberculous Mycobacteria (NTM)

Non-tuberculous mycobacteria (NTM) represent a diverse group of mycobacterial species known for causing opportunistic infections, especially in individuals with underlying health conditions. Unlike Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, NTM species exhibit different pathogenic characteristics and drug resistance mechanisms, making them increasingly relevant in clinical settings. PtkA is a crucial protein tyrosine kinase that regulates bacterial growth, stress response, and virulence by phosphorylating various substrates in Mtb. Understanding whether PtkA homologs exist in NTM could provide insights into their virulence and resistance mechanisms. In silico approaches, which utilize computational tools for sequence alignment, structure prediction, and functional annotation, offer a powerful means to identify homologous proteins across different species. In this article, we have employed tools like BLAST (Basic Local Alignment Search Tool), protein structure databases, and the NTM database to identify PtkA homologs in NTM genomes, providing a foundation for further studies.

Revisiting the functional annotation of TriTryp using sequence similarity tools

Trypanosomatids are the causative agents of deadly diseases in humans and livestock. Given the high phylogenetic distance of trypanosomatids from model organisms, these organisms have ample unannotated genes. Manual functional annotation is time-consuming, highlighting the importance of automated functional annotation tools. The development of automated functional tools is a hot research topic, and multiple tools have been developed for the task. PANNZER2 is an automated functional annotation tool that merely relies on the sequence similarity of the query to the annotated proteins. We tried PANNZER2 on Trypanosoma brucei, the most studied organism among trypanosomatids, to see if it could improve our knowledge of the functions of the genes.Even with the availability of automated annotation tools like InterPro2GO in databases such as TriTrypDB, PANNZER2 has made surprisingly confident predictions for some hypothetical proteins in T. brucei. In this study, we identify gaps in such annotations because of not employing pairwise sequence alignment tools in TriTrypDB's automated annotation process. Our findings demonstrate that even the use of stringent cutoffs can successfully annotate a significant number of proteins. Additionally, we discovered that adjusting the open reading frames in certain genes leads to sequences with increased sequence signature coverage—characterized by the length covered by at least one sequence signature—compared to the original sequences. This enhanced sequence signature coverage suggests these genomic fragments could be pseudogenes. To facilitate further exploration, we developed a script to help identify potential pseudogenes within an organism's genome, offering researchers a new tool for genomic analysis and understanding. We extended all our analysis to Trypanosoma cruzi and Leishmania major to assess the impact of this approach across different species.Our study demonstrates that by utilizing pairwise sequence similarity alignment, even with stringent cutoffs, we can attribute 2986, 3953, and 3798 new GO terms to the genomes of T. brucei, T. cruzi, and L. major. Additionally, we found that 210, 239, and 29 genes exhibit increased sequence signature coverage following frame correction, suggesting the presence of pseudogenes.

Bioinformatic Resources for Plant Genomic Research

Abstract: Genome assembly and annotation are crucial steps in plant genomics research as they provide valuable insights into plant genetic makeup, gene regulation, evolutionary history, and biological processes. In the emergence of high-throughput sequencing technologies, a plethora of genome assembly tools have been developed to meet the diverse needs of plant genome researchers. Choosing the most suitable tool to suit a specific research need can be daunting due to the complex and varied nature of plant genomes and reads from the sequencers. To assist informed decision-making in selecting the appropriate genome assembly and annotation tool(s), this review offers an extensive overview of the most widely used genome and transcriptome assembly tools. The review covers the specific information on each tool in tabular data, and the data types it can process. In addition, the review delves into transcriptome assembly tools, plant resource databases, and repositories (12 for Arabidopsis, 9 for Rice, 5 for Tomato, and 8 general use resources), which are vital for gene expression profiling and functional annotation and ontology tools that facilitate data integration and analysis.

In-silico approach to characterize the structure and function of a hypothetical protein of Monkeypox virus exploring Chordopox-A20R domain-containing protein activity.

Background: Monkeypox has emerged as a noteworthy worldwide issue due to its daily escalating case count. This illness presents diverse symptoms,including skin manifestations, which have the potential to spread through contact. The transmission of this infectious agent is intricate and readily transfers between individuals.Methods: The hypothetical protein MPXV-SI-2022V502225_00135 strain of monkeypox underwent structural and functional analysis using NCBI-CD Search, Pfam, and InterProScan. Quality assessment utilized PROCHECK, QMEAN, Verify3D, and ERRAT, followed by protein-ligand docking, visualization, and a 100-nanosecond simulation on Schrodinger Maestro.Results: Different physicochemical properties were estimated, indicating a stable molecular weight (49147.14) and theoretical pI (5.62) with functional annotation tools predicting the target protein to contain the domain of Chordopox_A20R domain. In secondary structure analysis, the helix coil was found to be predominant. The three-dimensional (3D) structure of the protein was obtained using a template protein (PDB ID:6zyc.1), which became more stable after YASARA energy minimization and was validated by quality assessment tools like PROCHECK, QMEAN, Verify3D, and ERRAT. Protein-ligand docking was conducted using PyRx 9.0 software to examine the binding and interactions between a ligand and a hypothetical protein, focusing on various amino acids. The model structure, active site, and binding site were visualized using the CASTp server, FTsite, and PyMOL. A 100 nanosecond simulation was performed with ligand CID_16124688 to evaluate the efficiency of this protein.Conclusion: The analysis revealed significant binding interactions and enhanced stability, aiding in drug or vaccine design for effective antiviral treatment and patient management.

Integrating transcriptomic and proteomic data for a comprehensive molecular perspective on the association between sarcopenia and osteoporosis

BackgroundOsteoporosis and sarcopenia are common age-related conditions characterized by the progressive loss of bone density and muscle mass, respectively. Their co-occurrence, often referred to as osteosarcopenia, presents significant challenges in elderly care due to increased fragility and functional impairment. Existing studies have identified shared pathological mechanisms between these conditions, including inflammation, hormonal imbalances, and metabolic dysregulation, but a comprehensive understanding of their molecular interplay remains incomplete. ObjectiveThis study aims to deepen our understanding of the molecular interactions between sarcopenia and osteoporosis through an integrated omics approach, revealing potential therapeutic targets and biomarkers. MethodsEmploying a combination of proteomics and transcriptomics analyses, this study analyzed bone and muscle tissue samples from patients diagnosed with osteoporosis and osteosarcopenia. Techniques included high-throughput sequencing and label-free proteomics, supported by advanced bioinformatics tools for data analysis and functional annotation of genes and proteins. ResultsThe study found marked differences in gene and protein expressions between osteoporosis and osteosarcopenia tissues. Specifically, genes like PDIA5, TUBB1, and CYFIP2 in bone, along with MYH7 and NCAM1 in muscle, exhibited differential expression at both mRNA and protein levels. Pathway analyses revealed the significance of oxidative-reduction balance, cellular metabolism, and immune response in the progression of these conditions. Importantly, the study pinpointed osteoclast differentiation and NF-kappa B signaling pathways as critical in the molecular dynamics of osteosarcopenia, suggesting potential targets for therapy. ConclusionsThis study utilized transcriptomics and proteomics to identify key genes and proteins impacting sarcopenia and osteoporosis, employing advanced network tools to delineate interaction networks and crucial signaling pathways. It highlighted genes like PDIA5 and TUBB1, consistently expressed in both analyses, involved in pathways such as osteoclast differentiation and cytokine interactions. These insights enhance understanding of the molecular interplay in bone and muscle degeneration with aging, suggesting directions for future research into therapeutic interventions and prevention strategies for age-related degenerative diseases.

Increased burden of rare protein-truncating variants in constrained, brain-specific and synaptic genes in extremely impulsively violent males with antisocial personality disorder.

The genetic correlates of extreme impulsive violence are poorly understood, and there have been few studies that have characterized a large group of affected individuals both clinically and genetically. We performed whole exome sequencing (WES) in 290 males with the life-course-persistent, extremely impulsively violent form of antisocial personality disorder (APD) and analyzed the spectrum of rare protein-truncating variants (rPTVs). Comparisons were made with 314 male controls and publicly available genotype data. Functional annotation tools were used for biological interpretation. Participants were significantly more likely to harbor rPTVs in genes that are intolerant to loss-of-function variants (odds ratio [OR] 2.06; p < 0.001), specifically expressed in brain (OR 2.80; p = 0.036) and enriched for those involved in neurotransmitter transport and synaptic processes. In 60 individuals (20%), we identified rPTVs that we classified as clinically relevant based on their clinical associations, biological function and gene expression patterns. Of these, 37 individuals harbored rPTVs in 23 genes that are associated with a monogenic neurological disorder, and 23 individuals harbored rPTVs in 20 genes reportedly intolerant to loss-of-function variants. The analysis presents evidence in support of a model where presence of either one or several private, functionally relevant mutations contribute significantly to individual risk of life-course-persistent APD and reveals multiple individuals who could be affected by clinically unrecognized neuropsychiatric Mendelian disease. Thus, Mendelian diseases and increased rPTV burden may represent important factors for the development of extremely impulsive violent life-course-persistent forms of APD irrespective of their clinical presentation.

Stochastic processes dominated the soil bacterial community assemblages along an altitudinal gradient in boreal forests

Soil bacteria, as integral components of the soil microbial community, play pivotal roles in biogeochemical cycles and ecosystem functions in boreal forests. The altitudinal patterns of soil bacteria have been widely reported, but their community assembly is uncertain. Here, we investigated the soil bacterial community attributes (diversity, taxonomic and functional composition, and bacterial interactions) and ecological processes associated with community assembly on Mt. Oakley in the northern Greater Khingan Mountains via Illumina MiSeq sequencing and functional annotation tools. The alpha diversity indices of the soil bacteria exhibited a progressively decreasing trend with increasing altitude across seasons. The relative abundance of the dominant bacterial taxa was more sensitive to altitude than to season. More complex (more nodes and links) bacterial interactions were detected at the lowest and highest altitudinal sites, as well as in September. The variations in the taxonomic and functional compositions of the soil bacteria induced by altitude were mainly driven by the variations in soil pH and extracellular enzyme activities. Stochastic (dispersal limitation and drift) processes largely controlled the soil bacterial community assemblages across spatiotemporal scales. The community assemblages of soil bacteria were affected by altitude-induced changes in the taxonomic composition and functional groups. Collectively, our results have significant implications for understanding bacterial biogeography and community assembly mechanisms along altitudinal gradients in boreal forest ecosystems.

Functional annotation of a divergent genome using sequence and structure-based similarity

BackgroundMicrosporidia are a large taxon of intracellular pathogens characterized by extraordinarily streamlined genomes with unusually high sequence divergence and many species-specific adaptations. These unique factors pose challenges for traditional genome annotation methods based on sequence similarity. As a result, many of the microsporidian genomes sequenced to date contain numerous genes of unknown function. Recent innovations in rapid and accurate structure prediction and comparison, together with the growing amount of data in structural databases, provide new opportunities to assist in the functional annotation of newly sequenced genomes.ResultsIn this study, we established a workflow that combines sequence and structure-based functional gene annotation approaches employing a ChimeraX plugin named ANNOTEX (Annotation Extension for ChimeraX), allowing for visual inspection and manual curation. We employed this workflow on a high-quality telomere-to-telomere sequenced tetraploid genome of Vairimorpha necatrix. First, the 3080 predicted protein-coding DNA sequences, of which 89% were confirmed with RNA sequencing data, were used as input. Next, ColabFold was used to create protein structure predictions, followed by a Foldseek search for structural matching to the PDB and AlphaFold databases. The subsequent manual curation, using sequence and structure-based hits, increased the accuracy and quality of the functional genome annotation compared to results using only traditional annotation tools. Our workflow resulted in a comprehensive description of the V. necatrix genome, along with a structural summary of the most prevalent protein groups, such as the ricin B lectin family. In addition, and to test our tool, we identified the functions of several previously uncharacterized Encephalitozoon cuniculi genes.ConclusionWe provide a new functional annotation tool for divergent organisms and employ it on a newly sequenced, high-quality microsporidian genome to shed light on this uncharacterized intracellular pathogen of Lepidoptera. The addition of a structure-based annotation approach can serve as a valuable template for studying other microsporidian or similarly divergent species.

Automatic annotation of the bHLH gene family in plants

BackgroundThe bHLH transcription factor family is named after the basic helix-loop-helix (bHLH) domain that is a characteristic element of their members. Understanding the function and characteristics of this family is important for the examination of a wide range of functions. As the availability of genome sequences and transcriptome assemblies has increased significantly, the need for automated solutions that provide reliable functional annotations is emphasised.ResultsA phylogenetic approach was adapted for the automatic identification and functional annotation of the bHLH transcription factor family. The bHLH_annotator, designed for the automated functional annotation of bHLHs, was implemented in Python3. Sequences of bHLHs described in literature were collected to represent the full diversity of bHLH sequences. Previously described orthologs form the basis for the functional annotation assignment to candidates which are also screened for bHLH-specific motifs. The pipeline was successfully deployed on the two Arabidopsis thaliana accessions Col-0 and Nd-1, the monocot species Dioscorea dumetorum, and a transcriptome assembly of Croton tiglium. Depending on the applied search parameters for the initial candidates in the pipeline, species-specific candidates or members of the bHLH family which experienced domain loss can be identified.ConclusionsThe bHLH_annotator allows a detailed and systematic investigation of the bHLH family in land plant species and classifies candidates based on bHLH-specific characteristics, which distinguishes the pipeline from other established functional annotation tools. This provides the basis for the functional annotation of the bHLH family in land plants and the systematic examination of a wide range of functions regulated by this transcription factor family.

A Novel Approach to Epigenetic Profiling in Acute Myeloid Leukemia: Mapping Methylation and Hydroxymethylation in Genomic and Cell-Free DNA

Introduction: Acute Myeloid Leukemia (AML) is marked by uncontrolled growth of undifferentiated myeloid cells. DNA methylation patterns in AML have been thoroughly researched, however, studies on hydroxy methylation patterns, indicating active DNA methylation erasure, are still evolving. Our principal goal was to detect differential methylation and hydroxy methylation regions between AML and non-malignant samples. We proposed that simultaneous profiling of blood cells and circulating cell-free DNA would offer more insights into the disease mechanism. Therefore, we combined these data sources to provide a comprehensive epigenetic picture, assuming that this dual approach could reveal new AML biomarkers and enhance our understanding regarding AML pathobiology. Methods: We collected peripheral blood samples from 16 newly diagnosed AML patients (median age 64, age range 36-85) treated at Tel Aviv Sourasky Medical Center, Israel, and 33 age-matched healthy volunteers (NCT05735704). We utilized novel microarray technology to examine methylation and hydroxy methylation patterns in both genomic and cell-free DNA. Following differential methylation region extraction, we used DAVID functional annotation tool to interpret our results' biological significance. Results: This report presents the preliminary methylation data results. Methylation analysis unveiled 474 and 475 differentially methylated regions between AML and controls in genomic and cell-free DNA analysis, respectively, with only 18 common regions. The clear AML-control distinction is displayed in hierarchical clustering heatmaps (Figure 1a, 1b). Further DAVID tool analysis revealed enrichment in biological processes such as aging, cell cycle regulation, cell morphogenesis, and MAP kinase activity - processes closely linked with AML. Notably, genomic data analysis showed significant enrichment of the ‘transcriptional misregulation in cancer’ pathway, involving genes encoding vital transcription factors for cell development and differentiation. Figure 2 demonstrates the biological pathways for genomic and cell free data analysis. Conclusions: This research underscores the importance of combined epigenetic data from malignant tissue and its microenvironment, obtained by examining genomic and cell-free DNA. Both datasets exhibit striking differences between AML and control samples. We detected enrichment of AML-related biological pathways within differential regions. Our findings of enriched biological pathways align with existing literature, reinforce our analysis validity, and support our combined view of methylation patterns in genomic and cell-free DNA. By suggesting a combined view of these two types of DNA, we provide a more comprehensive perspective on AML's complex epigenetic landscape. It underscores the value of such integrative epigenetic profiling in shedding new light on the intricacies of AML development and progression, which may eventually guide the discovery of novel therapeutic targets.

Evidential deep learning for trustworthy prediction of enzyme commission number.

The rapid growth of uncharacterized enzymes and their functional diversity urge accurate and trustworthy computational functional annotation tools. However, current state-of-the-art models lack trustworthiness on the prediction of the multilabel classification problem with thousands of classes. Here, we demonstrate that a novel evidential deep learning model (named ECPICK) makes trustworthy predictions of enzyme commission (EC) numbers with data-driven domain-relevant evidence, which results in significantly enhanced predictive power and the capability to discover potential new motif sites. ECPICK learns complex sequential patterns of amino acids and their hierarchical structures from 20million enzyme data. ECPICK identifies significant amino acids that contribute to the prediction without multiple sequence alignment. Our intensive assessment showed not only outstanding enhancement of predictive performance on the largest databases of Uniprot, Protein Data Bank (PDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG), but also a capability to discover new motif sites in microorganisms. ECPICK is a reliable EC number prediction tool to identify protein functions of an increasing number of uncharacterized enzymes.

Targeting of insulin receptor endocytosis as a treatment to insulin resistance

BackgroundInsulin resistance is the decreased effectiveness of insulin receptor function during signaling of glucose uptake. Insulin receptors are regulated by endocytosis, a process that removes receptors from the cell surface to be marked for degradation or for re-use. ObjectivesOur goal was to discover insulin-resistance-related genes that play key roles in endocytosis which could serve as potential biological targets to enhance insulin sensitivity. MethodsThe gene mutations related to insulin resistance were elucidated from ClinVar. These were used as the seed set. Using the GeneFriends program, the genes associated with this set were elucidated and used as an enriched set for the next step. The enriched gene set network was visualized by Cytoscape. After that, using the VisANT program, the most significant cluster of genes was identified. With the help of the DAVID program, the most important KEGG pathway corresponding to the gene cluster and insulin resistance was found. Eleven genes part of the KEGG endocytosis pathway were identified. Finally, using the ChEA3 program, seven transcription factors managing these genes were defined. ResultsThirty-two genes of pathogenic significance in insulin resistance were elucidated, and then co-expression data for these genes were utilized. These genes were organized into clusters, one of which was singled out for its high node count of 58 genes and low p-value (p = 4.117 × 10−7). DAVID Pathways, a functional annotation tool, helped identify a set of 11 genes from a single cluster associated with the endocytosis pathway related to insulin resistance. These genes (AMPH, BIN1, CBL, DNM1, DNM2, DNM3, ITCH, SH3GL1, SH3GL2, SH3GL3, and SH3KBP1) are all involved in either clathrin-mediated endocytosis of the insulin receptor (IR) or clathrin-independent endocytosis of insulin-resistance-related G protein-coupled receptors (GPCR). They represent prime therapeutic targets to improve insulin sensitivity through modulation of transmembrane cell signaling. Using the ChEA3 database, we also found seven transcription factors (REST, MYPOP, CAMTA2, MYT1L, ZBTB18, NKX6–2, and CXXC5) that control the expression of these 11 genes. Inhibiting these key transcription factors would be another strategy to downregulate endocytosis. ConclusionWe believe that delaying removal of insulin receptors from the cell surface would prolong signaling of glucose uptake and counteract the symptoms of insulin resistance.

Domain Architecture Based Methods for Comparative Functional Genomics Toward Therapeutic Drug Target Discovery.

Genes duplicate, mutate, recombine, fuse or fission to produce new genes, or when genes are formed from de novo, novel functions arise during evolution. Researchers have tried to quantify the causes of these molecular diversification processes to know how these genes increase molecular complexity over a period of time, for instance protein domain organization. In contrast to global sequence similarity, protein domain architectures can capture key structural and functional characteristics, making them better proxies for describing functional equivalence. In Prokaryotes and eukaryotes it has proven that, domain designs are retained over significant evolutionary distances. Protein domain architectures are now being utilized to categorize and distinguish evolutionarily related proteins and find homologs among species that are evolutionarily distant from one another. Additionally, structural information stored in domain structures has accelerated homology identification and sequence search methods. Tools for functional protein annotation have been developed to discover, protein domain content, domain order, domain recurrence, and domain position as all these contribute to the prediction of protein functional accuracy. In this review, an attempt is made to summarise facts and speculations regarding the use of protein domain architecture and modularity to identify possible therapeutic targets among cellular activities based on the understanding their linked biological processes.

Plant GARDEN: a portal website for cross-searching between different types of genomic and genetic resources in a wide variety of plant species

BackgroundPlant genome information is fundamental to plant research and development. Along with the increase in the number of published plant genomes, there is a need for an efficient system to retrieve various kinds of genome-related information from many plant species across plant kingdoms. Various plant databases have been developed, but no public database covers both genomic and genetic resources over a wide range of plant species.Main bodyWe have developed a plant genome portal site, Plant GARDEN (Genome And Resource Database Entry: https://plantgarden.jp/en/index), to provide diverse information related to plant genomics and genetics in divergent plant species. Elasticsearch is used as a search engine, and cross-keyword search across species is available. Web-based user interfaces (WUI) for PCs and tablet computers were independently developed to make data searches more convenient. Several types of data are stored in Plant GARDEN: reference genomes, gene sequences, PCR-based DNA markers, trait-linked DNA markers identified in genetic studies, SNPs, and in/dels on publicly available sequence read archives (SRAs). The data registered in Plant GARDEN as of March 2023 included 304 assembled genome sequences, 11,331,614 gene sequences, 419,132 DNA markers, 8,225 QTLs, and 5,934 SNP lists (gvcf files). In addition, we have re-annotated all the genes registered in Plant GARDEN by using a functional annotation tool, Hayai-Annotation, to compare the orthologous relationships among genes.ConclusionThe aim of Plant GARDEN is to provide plant genome information for use in the fields of plant science as well as for plant-based industries, education, and other relevant areas. Therefore, we have designed a WUI that allows a diverse range of users to access such information in an easy-to-understand manner. Plant GARDEN will eventually include a wide range of plant species for which genome sequences are assembled, and thus the number of plant species in the database will continue to expand. We anticipate that Plant GARDEN will promote the understanding of genomes and gene diversity by facilitating comparisons of the registered sequences.

Xanthine oxidoreductase gene polymorphisms are associated with high risk of sepsis and organ failure

BackgroundSepsis and associated organ failures confer substantial morbidity and mortality. Xanthine oxidoreductase (XOR) is implicated in the development of tissue oxidative damage in a wide variety of respiratory and cardiovascular disorders including sepsis and sepsis-associated acute respiratory distress syndrome (ARDS). We examined whether single nucleotide polymorphisms (SNPs) in the XDH gene (encoding XOR) might influence susceptibility to and outcome in patients with sepsis.MethodsWe genotyped 28 tag SNPs in XDH gene in the CELEG cohort, including 621 European American (EA) and 353 African American (AA) sepsis patients. Serum XOR activity was measured in a subset of CELEG subjects. Additionally, we assessed the functional effects of XDH variants utilizing empirical data from different integrated software tools and datasets.ResultsAmong AA patients, six intronic variants (rs206805, rs513311, rs185925, rs561525, rs2163059, rs13387204), in a region enriched with regulatory elements, were associated with risk of sepsis (P < 0.008–0.049). Two out of six SNPs (rs561525 and rs2163059) were associated with risk of sepsis-associated ARDS in an independent validation cohort (GEN-SEP) of 590 sepsis patients of European descent. Two common SNPs (rs1884725 and rs4952085) in tight linkage disequilibrium (LD) provided strong evidence for association with increased levels of serum creatinine (Padjusted<0.0005 and 0.0006, respectively), suggesting a role in increased risk of renal dysfunction. In contrast, among EA ARDS patients, the missense variant rs17011368 (I703V) was associated with enhanced mortality at 60-days (P < 0.038). We found higher serum XOR activity in 143 sepsis patients (54.5 ± 57.1 mU/mL) compared to 31 controls (20.9 ± 12.4 mU/mL, P = 1.96 × 10− 13). XOR activity was associated with the lead variant rs185925 among AA sepsis patients with ARDS (P < 0.005 and Padjusted<0.01). Multifaceted functions of prioritized XDH variants, as suggested by various functional annotation tools, support their potential causality in sepsis.ConclusionsOur findings suggest that XOR is a novel combined genetic and biochemical marker for risk and outcome in patients with sepsis and ARDS.

GFAP: ultra-fast and accurate gene functional annotation software for plants.

GFAP is a gene functional annotation tool for myriad plant studies.

Graph Theory in DNA Sequencing: Unveiling Genetic Patterns

Graph theory, a branch of mathematics that studies the properties and relationships of graphs, has emerged as a powerful tool in addressing the complexities of DNA sequencing. This paper highlights the application of graph theory in DNA sequencing and its implications in various aspects of genomics research. One fundamental concept in graph theory applied to DNA sequencing is the construction of the de Bruijn graph. This graph represents overlapping k-mers, subsequences of length k, as nodes, with edges connecting adjacent k-mers. By constructing and analyzing the de Bruijn graph, researchers can infer the underlying DNA sequence, detect errors, resolve repetitive regions, and identify structural variations in the genome. Graph algorithms such as Overlap path, Eulerian path and Hamiltonian path have been adapted to reconstruct complete DNA sequences from fragmented reads obtained through sequencing. These algorithms leverage the connectivity information present in the de Bruijn graph to traverse and assemble the reads, enabling the reconstruction of long DNA sequences accurately. the application of graph theory in DNA sequencing has revolutionized the field of genomics by providing powerful computational tools for DNA assembly, sequence analysis, and functional annotation. By leveraging graph theory concepts and algorithms, researchers can unravel the intricate information embedded within DNA sequences, leading to deeper insights into the genetic basis of life and its applications in various fields, including medicine, agriculture, and evolutionary biology. Future developments in graph-based algorithms and computational techniques hold promise for further enhancing our ability to unlock the secrets encoded within the vast realm of DNA sequences.

Functional Profiling and Evolutionary Analysis of a Marine Microalgal Virus Pangenome.

Phycodnaviridae are large double-stranded DNA viruses, which facilitate studies of host-virus interactions and co-evolution due to their prominence in algal infection and their role in the life cycle of algal blooms. However, the genomic interpretation of these viruses is hampered by a lack of functional information, stemming from the surprising number of hypothetical genes of unknown function. It is also unclear how many of these genes are widely shared within the clade. Using one of the most extensively characterized genera, Coccolithovirus, as a case study, we combined pangenome analysis, multiple functional annotation tools, AlphaFold structural modeling, and literature analysis to compare the core and accessory pangenome and assess support for novel functional predictions. We determined that the Coccolithovirus pangenome shares 30% of its genes with all 14 strains, making up the core. Notably, 34% of its genes were found in at most three strains. Core genes were enriched in early expression based on a transcriptomic dataset of Coccolithovirus EhV-201 algal infection, were more likely to be similar to host proteins than the non-core set, and were more likely to be involved in vital functions such as replication, recombination, and repair. In addition, we generated and collated annotations for the EhV representative EhV-86 from 12 different annotation sources, building up information for 142 previously hypothetical and putative membrane proteins. AlphaFold was further able to predict structures for 204 EhV-86 proteins with a modelling accuracy of good-high. These functional clues, combined with generated AlphaFold structures, provide a foundational framework for the future characterization of this model genus (and other giant viruses) and a further look into the evolution of the Coccolithovirus proteome.

Functional Annotation Routines Used by ABRF Bioinformatics Core Facilities - Observations, Comparisons, and Considerations.

The functional annotation of gene lists is a common analysis routine required for most genomics experiments, and bioinformatics core facilities must support these analyses. In contrast to methods such as the quantitation of RNA-Seq reads or differential expression analysis, our research group noted a lack of consensus in our preferred approaches to functional annotation. To investigate this observation, we selected 4 experiments that represent a range of experimental designs encountered by our cores and analyzed those data with 6 tools used by members of the Association of Biomolecular Resource Facilities (ABRF) Genomic Bioinformatics Research Group (GBIRG). To facilitate comparisons between tools, we focused on a single biological result for each experiment. These results were represented by a gene set, and we analyzed these gene sets with each tool considered in our study to map the result to the annotation categories presented by each tool. In most cases, each tool produces data that would facilitate identification of the selected biological result for each experiment. For the exceptions, Fisher's exact test parameters could be adjusted to detect the result. Because Fisher's exact test is used by many functional annotation tools, we investigated input parameters and demonstrate that, while background set size is unlikely to have a significant impact on the results, the numbers of differentially expressed genes in an annotation category and the total number of differentially expressed genes under consideration are both critical parameters that may need to be modified during analyses. In addition, we note that differences in the annotation categories tested by each tool, as well as the composition of those categories, can have a significant impact on results.

Deep thermal profiling for detection of functional proteoform groups.

The complexity of the functional proteome extends considerably beyond the coding genome, resulting in millions of proteoforms. Investigation of proteoforms and their functional roles is important to understand cellular physiology and its deregulation in diseases but challenging to perform systematically. Here we applied thermal proteome profiling with deep peptide coverage to detect functional proteoform groups in acute lymphoblastic leukemia cell lines with different cytogenetic aberrations. We detected 15,846 proteoforms, capturing differently spliced, cleaved and post-translationally modified proteins expressed from 9,290 genes. We identified differential co-aggregation of proteoform pairs and established links to disease biology. Moreover, we systematically made use of measured biophysical proteoform states to find specific biomarkers of drug sensitivity. Our approach, thus, provides a powerful and unique tool for systematic detection and functional annotation of proteoform groups.