Proper Annotation Research Articles

An Educational Bioinformatics Project to Improve Genome Annotation.

Scientific advancement is hindered without proper genome annotation because biologists lack a complete understanding of cellular protein functions. In bacterial cells, hypothetical proteins (HPs) are open reading frames with unknown functions. HPs result from either an outdated database or insufficient experimental evidence (i.e., indeterminate annotation). While automated annotation reviews help keep genome annotation up to date, often manual reviews are needed to verify proper annotation. Students can provide the manual review necessary to improve genome annotation. This paper outlines an innovative classroom project that determines if HPs have outdated or indeterminate annotation. The Hypothetical Protein Characterization Project uses multiple well-documented, freely available, web-based, bioinformatics resources that analyze an amino acid sequence to (1) detect sequence similarities to other proteins, (2) identify domains, (3) predict tertiary structure including active site characterization and potential binding ligands, and (4) determine cellular location. Enough evidence can be generated from these analyses to support re-annotation of HPs or prioritize HPs for experimental examinations such as structural determination via X-ray crystallography. Additionally, this paper details several approaches for selecting HPs to characterize using the Hypothetical Protein Characterization Project. These approaches include student- and instructor-directed random selection, selection using differential gene expression from mRNA expression data, and selection based on phylogenetic relations. This paper also provides additional resources to support instructional use of the Hypothetical Protein Characterization Project, such as example assignment instructions with grading rubrics, links to training videos in YouTube, and several step-by-step example projects to demonstrate and interpret the range of achievable results that students might encounter. Educational use of the Hypothetical Protein Characterization Project provides students with an opportunity to learn and apply knowledge of bioinformatic programs to address scientific questions. The project is highly customizable in that HP selection and analysis can be specifically formulated based on the scope and purpose of each student’s investigations. Programs used for HP analysis can be easily adapted to course learning objectives. The project can be used in both online and in-seat instruction for a wide variety of undergraduate and graduate classes as well as undergraduate capstone, honor’s, and experiential learning projects.

Database limitations for studying the human gut microbiome.

BackgroundIn the last twenty years, new methodologies have made possible the gathering of large amounts of data concerning the genetic information and metabolic functions associated to the human gut microbiome. In spite of that, processing all this data available might not be the simplest of tasks, which could result in an excess of information awaiting proper annotation. This assessment intended on evaluating how well respected databases could describe a mock human gut microbiome.MethodsIn this work, we critically evaluate the output of the cross–reference between the Uniprot Knowledge Base (Uniprot KB) and the Kyoto Encyclopedia of Genes and Genomes Orthologs (KEGG Orthologs) or the evolutionary genealogy of genes: Non-supervised Orthologous groups (EggNOG) databases regarding a list of species that were previously found in the human gut microbiome.ResultsFrom a list which contemplates 131 species and 52 genera, 53 species and 40 genera had corresponding entries for KEGG Database and 82 species and 47 genera had corresponding entries for EggNOG Database. Moreover, we present the KEGG Orthologs (KOs) and EggNOG Orthologs (NOGs) entries associated to the search as their distribution over species and genera and lists of functions that appeared in many species or genera, the “core” functions of the human gut microbiome. We also present the relative abundance of KOs and NOGs throughout phyla and genera. Lastly, we expose a variance found between searches with different arguments on the database entries. Inferring functionality based on cross-referencing UniProt and KEGG or EggNOG can be lackluster due to the low number of annotated species in Uniprot and due to the lower number of functions affiliated to the majority of these species. Additionally, the EggNOG database showed greater performance for a cross-search with Uniprot about a mock human gut microbiome. Notwithstanding, efforts targeting cultivation, single-cell sequencing or the reconstruction of high-quality metagenome-assembled genomes (MAG) and their annotation are needed to allow the use of these databases for inferring functionality in human gut microbiome studies.

Tool Flank Wear Estimation using Emitted Sound Signal Analysis by PCA – SER Based Peak to Peak Measurements

The higher levels degrees of automation for industry 4.0 standards require optimization techniques in production activities including tool wear monitoring. The unmonitored tool may spoil the product if it is worn out more than the permitted levels or micro broken or cracked internally. A novel method suggested in this work utilizes neither extra ordinary calculation nor complex mathematical transformations in tool wear monitoring. This method follows no video capturing and image processing rather follows a simple sound wave monitoring captured at the time conversion process by a microphone. The SER a PCA variant technique with the purpose of used in selecting simply the higher velocity of principal components (PCs) in quantifying the feature extracted while separating noise from sound signals. A SER method is used for the selection of suitable PCs for consideration. The best methods of normalization suitable for the SER method is found and implemented the PCA-SER on signals after filter the signals by butter worth filter to remove noise. This proposed procedure resulted in wide differences and proper annotation in differentiating the degree of tool wear in fresh, slight and severely worn categories.

Identification of modules and functional analysis in CRC subtypes by integrated bioinformatics analysis.

Colorectal cancer is one of the top three causes of cancer-related mortality globally, but no predictive molecular biomarkers are currently available for identifying the disease stage of colorectal cancer patients. Common molecular patterns in the disease, beyond superficial manifestations, can be significant in determining treatment choices. In this study, we used microarray data from colorectal cancer and adjacent normal tissue from the GEO database. These data were categorized into four consensus molecular subtypes based on distinct gene expression signatures. Weighted gene-based protein–protein interaction network analysis was performed for each subtype. NUSAP1, CD44, and COL4A1 modules were found to be statistically significant and present among all the subtypes and displayed though similar but not identical functional enrichment results. Reference of the characteristics of the subtypes to functional modules is necessary since the latter can stay resistant to platform changes and technique noise when compared with other analyses. The CMS4-mesenchymal group, which currently has a poor prognosis, was examined in the study. It is composed mainly of genes involved in immune and stromal expression, with modules focused on ECM dysregulation and chemokine biological processes. Hub genes detection and its’ mapping into the protein–protein interaction network can be indicative of possible targets against specific modules. This approach identified subtypes using enrichment-oriented analysis in functional modules. Proper annotation of functional analysis of modules from different subtypes of CRC might be directive for finding extra options for treatment targets and guiding clinical routines.

De novo Assembly and Analysis of Amur Sturgeon (Acipenser schrenckii) Transcriptome in Response to Mycobacterium Marinum Infection to Identify Putative Genes Involved in Immunity.

Fish mycobacteriosis is a common bacterial disease in many species of freshwater and marine fish and has caused severe loss of fish production. Mycobacterium marinum has been the most prevalent pathogen observed in several outbreaks of mycobacteriosis of farmed sturgeons in China. However, the immune responses and pathology of sturgeons in mycobacterial infection are rarely studied. Therefore, we used the Illumina RNA-seq method to analyze the transcriptome profile of Acipenser schrenckii challenged with Mycobacterium marinum. To begin, 168,220 non-redundant contigs were acquired from the infection and control groups, and among these, 33,225 contigs have acquired annotations. A total of 4,043 differently expressed (DE) contigs between the two groups were identified, and among these, 2479 were upregulated and 1564 were down-regulated in the infected fish. A total of 1,340 DE contigs with acquired annotations in KEGG were enriched for 124 pathways including the TNF signaling pathway, and the Toll-like receptor signaling pathway. The roles of DE genes involved in significant pathways and other processes were discussed. The 2,209 DE contigs that have yet to acquire proper annotation may represent candidate genes associated with infection in sturgeons and are expected to serve as immunogenetic resources for further study. To our best knowledge, this is the first transcriptome study on sturgeons under bacterial infection.

Carboxypeptidase in prolyl oligopeptidase family: Unique enzyme activation and substrate-screening mechanisms

Serine peptidases of the prolyl oligopeptidase (POP) family are of substantial therapeutic importance because of their involvement in diseases such as diabetes, cancer, neurological diseases, and autoimmune disorders. Proper annotation and knowledge of substrate specificity mechanisms in this family are highly valuable. Although endopeptidase, dipeptidyl peptidase, tripeptidyl peptidase, and acylaminoacyl peptidase activities have been reported previously, here we report the first instance of carboxypeptidase activity in a POP family member. We determined the crystal structures of this carboxypeptidase, an S9C subfamily member from Deinococcus radiodurans, in its active and inactive states at 2.3-Å resolution, providing an unprecedented view of assembly and disassembly of the active site mediated by an arginine residue. We observed that this residue is poised to bind substrate in the active structure and disrupts the catalytic triad in the inactive structure. The assembly of the active site is accompanied by the ordering of gating loops, which reduces the effective size of the oligomeric pore. This prevents the entry of larger peptides and constitutes a novel mechanism for substrate screening. Furthermore, we observed structural adaptations that enable its carboxypeptidase activity, with a unique loop and two arginine residues in the active site cavity orienting the peptide substrate for catalysis. Using these structural features, we identified homologs of this enzyme in the POP family and confirmed the presence of carboxypeptidase activity in one of them. In conclusion, we have identified a new type within POP enzymes that exhibits not only unique activity but also a novel substrate-screening mechanism.

An Analysis of Sindhi Annotated Corpus using Supervised Machine Learning Methods

The linguistic corpus of Sindhi language is significant for computational linguistics process, machine learning process, language features identification and analysis, semantic and sentiment analysis, information retrieval and so on. There is little computational linguistics work done on Sindhi text whereas, English, Arabic, Urdu and some other languages are fully resourced computationally. The grammar and morphemes of these languages are analyzed properly using dissimilar machine learning methods. The development and research work regarding computational linguistics are in progress on Sindhi language at this time. This study is planned to develop the Sindhi annotated corpus using universal POS (Part of Speech) tag set and Sindhi POS tag set for the purpose of language features and variation analysis. The features are extracted using TF-IDF (Term Frequency and Inverse Document Frequency) technique. The supervised machine learning model is developed to assess the annotated corpus to know the grammatical annotation of Sindhi language. The model is trained with 80% of annotated corpus and tested with 20% of test set. The cross-validation technique with 10-folds is utilized to evaluate and validate the model. The results of model show the better performance of model as well as confirm the proper annotation to Sindhi corpus. This study described a number of research gaps to work more on topic modeling, language variation, sentiment and semantic analysis of Sindhi language.

Functional Annotation and Analysis of Dual Oxidase 1 (DUOX1): a Potential Anti-pyocyanin Immune Component.

Dual Oxidase 1 (DUOX1) is a prominent immune system component primarily expressed in esophagus, lungs, skin, and urinary bladder including others. DUOX1 is involved in lactoperoxidase-mediated innate immunity at mucosal surfaces by generation of antimicrobial hypothiocyanite at the apical surface of epithelial lining. Upon detection of bacterial pathogens mainly Pseudomonas aeruginosa, DUOX1 is activated in bronchial epithelial cells. Both the host and pathogen enter a redox dual with DUOX1 and hypothiocyanite from host and Pyocyanin (PCN) as a redox active virulence factor from P. aeruginosa. The synergy of the both enzymes permanently oxidizes PCN and thus holds the potential to prevent PCN-induced virulence, which otherwise paves the way for establishment of persistent chronic infection. In this study, we structurally and functionally annotated the DUOX1, predicted its 3d structure, physio-chemical properties, post-translational modifications, and genetic polymorphism analysis with subsequent disease-associated single-nucleotide variations and their impact on DUOX1 functionality by employing in silico approaches. DUOX1 holds greater homology with gorilla and chimpanzee than other primates. The localization signal peptide was present at the beginning of the peptide with cleavage site at 22 aa position. Three distinct functional domains were observed based on homology: An_peroxidase, FRQ1, and oxido-reductase domains. Polymorphism analysis revealed > 60 SNPs associated with different cancers with probable damaging effects. No cancer-associated methylated island was observed for DUOX1. Three-dimensional structure was developed via homology modeling strategy. The proper annotation will help in characterization of DUOX1 and enhance our knowledge of its functionality and biological roles.

Using social media for classifying actionable insights in disaster scenario

Micro-blogging sites are important source of real-time situational information during disasters such as earthquakes, hurricanes, wildfires, flood etc. Such disasters cause miseries in the lives of affected people. Timely identification of steps needed to help the affected people in such situations can mitigate those miseries to a large extent. In this paper, we focus on the problem of automated classification of disaster related tweets to a set of predefined categories. Some example categories considered are resource availability, resource requirement, infrastructure damage etc. Proper annotation of the tweets with these class information can help in timely determination of the steps needed to be taken to address the concerns of the people in the affected areas. Depending on the information category, different feature sets might be useful for proper identification of posts belonging to that category. In this work, we define multiple feature sets and use them with various supervised classification algorithms from literature to study the effectiveness of our approach in annotating the tweets with their appropriate information categories.

A Framework for Building an Arabic Multi-disciplinary Ontology from Multiple Resources

Over recent years, the Internet has become people’s main source of information, with many databases and web pages being added and accessed every day. This continued growth in the amount of information available has led to frustration and difficulty for those attempting to find a specific piece of information. As such, many techniques are widely used to retrieve useful information and to mine valuable data; indeed, these techniques make it possible to discover hidden relations and patterns. Most of the above-mentioned techniques have been used primarily to process and analyse English text, but not Arabic text. Limited Arabic resources (e.g. datasets, databases, and ontologies), also make analysing and processing Arabic text a difficult task. As such, in this paper, we propose a framework for building an Arabic ontology from multiple resources. Thus, we will first extract and build an Arabic ontology from a publicly available directory, following which, we will enhance this ontology with rich data from the Internet. We will then use an Arabic online directory to construct a multi-disciplinary ontology that provides a hierarchical representation of topics in a conceptual way. Following this, we introduce an enhanced technique to enrich these ontologies with sufficient information and proper annotation for each concept. Finally, by using common information retrieval evaluation techniques, we confirm the viability of the proposed approach.

IMet: A Network-Based Computational Tool To Assist in the Annotation of Metabolites from Tandem Mass Spectra

Structural annotation of metabolites relies mainly on tandem mass spectrometry (MS/MS) analysis. However, approximately 90% of the known metabolites reported in metabolomic databases do not have annotated spectral data from standards. This situation has fostered the development of computational tools that predict fragmentation patterns in silico and compare these to experimental MS/MS spectra. However, because such methods require the molecular structure of the detected compound to be available for the algorithm, the identification of novel metabolites in organisms relevant for biotechnological and medical applications remains a challenge. Here, we present iMet, a computational tool that facilitates structural annotation of metabolites not described in databases. iMet uses MS/MS spectra and the exact mass of an unknown metabolite to identify metabolites in a reference database that are structurally similar to the unknown metabolite. The algorithm also suggests the chemical transformation that converts the known metabolites into the unknown one. As a proxy for the structural annotation of novel metabolites, we tested 148 metabolites following a leave-one-out cross-validation procedure or by using MS/MS spectra experimentally obtained in our laboratory. We show that for 89% of the 148 metabolites at least one of the top four matches identified by iMet enables the proper annotation of the unknown metabolites. To further validate iMet, we tested 31 metabolites proposed in the 2012-16 CASMI challenges. iMet is freely available at http://imet.seeslab.net .

Computational Functional Analysis of Lipid Metabolic Enzymes.

The computational analysis of enzymes that participate in lipid metabolism has both common and unique challenges when compared to the whole protein universe. Some of the hurdles that interfere with the functional annotation of lipid metabolic enzymes that are common to other pathways include the definition of proper starting datasets, the construction of reliable multiple sequence alignments, the definition of appropriate evolutionary models, and the reconstruction of phylogenetic trees with high statistical support, particularly for large datasets. Most enzymes that take part in lipid metabolism belong to complex superfamilies with many members that are not involved in lipid metabolism. In addition, some enzymes that do not have sequence similarity catalyze similar or even identical reactions. Some of the challenges that, albeit not unique, are more specific to lipid metabolism refer to the high compartmentalization of the routes, the catalysis in hydrophobic environments and, related to this, the function near or in biological membranes.In this work, we provide guidelines intended to assist in the proper functional annotation of lipid metabolic enzymes, based on previous experiences related to the phospholipase D superfamily and the annotation of the triglyceride synthesis pathway in algae. We describe a pipeline that starts with the definition of an initial set of sequences to be used in similarity-based searches and ends in the reconstruction of phylogenies. We also mention the main issues that have to be taken into consideration when using tools to analyze subcellular localization, hydrophobicity patterns, or presence of transmembrane domains in lipid metabolic enzymes.

Evolution and Diversity of Transposable Elements in Vertebrate Genomes.

Transposable elements (TEs) are selfish genetic elements that mobilize in genomes via transposition or retrotransposition and often make up large fractions of vertebrate genomes. Here, we review the current understanding of vertebrate TE diversity and evolution in the context of recent advances in genome sequencing and assembly techniques. TEs make up 4–60% of assembled vertebrate genomes, and deeply branching lineages such as ray-finned fishes and amphibians generally exhibit a higher TE diversity than the more recent radiations of birds and mammals. Furthermore, the list of taxa with exceptional TE landscapes is growing. We emphasize that the current bottleneck in genome analyses lies in the proper annotation of TEs and provide examples where superficial analyses led to misleading conclusions about genome evolution. Finally, recent advances in long-read sequencing will soon permit access to TE-rich genomic regions that previously resisted assembly including the gigantic, TE-rich genomes of salamanders and lungfishes.

YeATSAM analysis of the walnut and chickpea transcriptome reveals key genes undetected by current annotation tools

Background: The transcriptome, a treasure trove of gene space information, remains severely under-used by current genome annotation methods. Methods: Here, we present an annotation method in the YeATS suite (YeATSAM), based on information encoded by the transcriptome, that demonstrates artifacts of the assembler, which must be addressed to achieve proper annotation. Results and Discussion: YeATSAM was applied to the transcriptome obtained from twenty walnut tissues and compared to MAKER-P annotation of the recently published walnut genome sequence (WGS). MAKER-P and YeATSAM both failed to annotate several hundred proteins found by the other. Although many of these unannotated proteins have repetitive sequences (possibly transposable elements), other crucial proteins were excluded by each method. An egg cell-secreted protein and a homer protein were undetected by YeATSAM, although these did not produce any transcripts. Importantly, MAKER-P failed to classify key photosynthesis-related proteins, which we show emanated from Trinity assembly artifacts potentially not handled by MAKER-P. Also, no proteins from the large berberine bridge enzyme (BBE) family were annotated by MAKER-P. BBE is implicated in biosynthesis of several alkaloids metabolites, like anti-microbial berberine. As further validation, YeATSAM identified ~1000 genes that are not annotated in the NCBI database by Gnomon. YeATSAM used a RNA-seq derived chickpea ( Cicer arietinum L.) transcriptome assembled using Newbler v2.3. Conclusions: Since the current version of YeATSAM does not have an ab initio module, we suggest a combined annotation scheme using both MAKER-P and YeATSAM to comprehensively and accurately annotate the WGS.

Point of interest mining with proper semantic annotation

Mining geo-tagged social photo media has received large amounts of attention from researchers recently. Points of interest (POI) mining from a collection of geo-tagged photos is one of these problems. POI mining refers to the processes of pattern recognition (namely clustering), extraction and semantic annotation. However, based on unsupervised clustering methods, many POIs might not be mined. Additionally, there is a great challenge for the proper semantic annotation to data clusters after clustering. In practice, there are many applications which require the accuracy of semantic annotation and high quality of pattern recognition such as POI recommendation. In this paper, we study POI mining from a collection of geo-tagged photos in combination with proper semantic annotation by using additional POI information from high coverage external POI databases. We propose a novel POI mining framework by using two-level clustering, random walk and constrained clustering. In random walk clustering step, we separate a large-scale collection of geo-tagged photos into many clusters. In the constrained clustering step, we continue to divide the clusters that include many POIs into many sub-clusters, where the geo-tagged photos in a sub-cluster associate with a particular POI. Experimental results on two datasets of geo-tagged Flickr photos of two cities in California, USA have shown that the proposed method substantially outperforms existing approaches that are adapted to handle the problem.

YeATSAM analysis of the walnut and chickpea transcriptome reveals key genes undetected by current annotation tools.

Background: The transcriptome, a treasure trove of gene space information, remains severely under-used by current genome annotation methods. Methods: Here, we present an annotation method in the YeATS suite (YeATSAM), based on information encoded by the transcriptome, that demonstrates artifacts of the assembler, which must be addressed to achieve proper annotation. Results and Discussion:YeATSAM was applied to the transcriptome obtained from twenty walnut tissues and compared to MAKER-P annotation of the recently published walnut genome sequence (WGS). MAKER-P and YeATSAM both failed to annotate several hundred proteins found by the other. Although many of these unannotated proteins have repetitive sequences (possibly transposable elements), other crucial proteins were excluded by each method. An egg cell-secreted protein and a homer protein were undetected by YeATSAM, although these did not produce any transcripts. Importantly, MAKER-P failed to classify key photosynthesis-related proteins, which we show emanated from Trinity assembly artifacts potentially not handled by MAKER-P. Also, no proteins from the large berberine bridge enzyme (BBE) family were annotated by MAKER-P. BBE is implicated in biosynthesis of several alkaloids metabolites, like anti-microbial berberine. As further validation, YeATSAM identified ~1000 genes that are not annotated in the NCBI database by Gnomon. YeATSAM used a RNA-seq derived chickpea ( Cicer arietinum L.) transcriptome assembled using Newbler v2.3. Conclusions:Since the current version of YeATSAM does not have an ab initio module, we suggest a combined annotation scheme using both MAKER-P and YeATSAM to comprehensively and accurately annotate the WGS.

Next-Generation Sequencing and the Crustacean Immune System: The Need for Alternatives in Immune Gene Annotation.

Next-generation sequencing has been a huge benefit to investigators studying non-model species. High-throughput gene expression studies, which were once restricted to animals with extensive genomic resources, can now be applied to any species. Transcriptomic studies using RNA-Seq can discover hundreds of thousands of transcripts from any species of interest. The power and limitation of these techniques is the sheer size of the dataset that is acquired. Parsing these large datasets is becoming easier as more bioinformatic tools are available for biologists without extensive computer programming expertise. Gene annotation and physiological pathway tools such as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology enable the application of the vast amount of information acquired from model organisms to non-model species. While noble in nature, utilization of these tools can inadvertently misrepresent transcriptomic data from non-model species via annotation omission. Annotation followed by molecular pathway analysis highlights pathways that are disproportionately affected by disease, stress, or the physiological condition being examined. Problems occur when gene annotation procedures only recognizes a subset, often 50% or less, of the genes differently expressed from a non-model organisms. Annotated transcripts normally belong to highly conserved metabolic or regulatory genes that likely have a secondary or tertiary role, if any at all, in immunity. They appear to be disproportionately affected simply because conserved genes are most easily annotated. Evolutionarily induced specialization of physiological pathways is a driving force of adaptive evolution, but it results in genes that have diverged sufficiently to prevent their identification and annotation through conventional gene or protein databases. The purpose of this manuscript is to highlight some of the challenges faced when annotating crustacean immune genes by using an American lobster (Homarus americanus) transcriptome as an example. Immune genes have evolved rapidly over time, facilitating speciation and adaption to highly divergent ecological niches. Complete and proper annotation of immune genes from invertebrates has been challenging. Modulation of the crustacean immune system occurs in a variety of physiological responses including biotic and abiotic stressors, molting and reproduction. A simple method for the identification of a greater number of potential immune genes is proposed, along with a short introductory primer on crustacean immune response. The intended audience is not the advanced bioinformatic user, but those investigating physiological responses who require rudimentary understanding of crustacean immunological principles, but where immune gene regulation is not their primary interest.

Integrative Tissue-Specific Functional Annotations in the Human Genome Provide Novel Insights on Many Complex Traits and Improve Signal Prioritization in Genome Wide Association Studies.

Extensive efforts have been made to understand genomic function through both experimental and computational approaches, yet proper annotation still remains challenging, especially in non-coding regions. In this manuscript, we introduce GenoSkyline, an unsupervised learning framework to predict tissue-specific functional regions through integrating high-throughput epigenetic annotations. GenoSkyline successfully identified a variety of non-coding regulatory machinery including enhancers, regulatory miRNA, and hypomethylated transposable elements in extensive case studies. Integrative analysis of GenoSkyline annotations and results from genome-wide association studies (GWAS) led to novel biological insights on the etiologies of a number of human complex traits. We also explored using tissue-specific functional annotations to prioritize GWAS signals and predict relevant tissue types for each risk locus. Brain and blood-specific annotations led to better prioritization performance for schizophrenia than standard GWAS p-values and non-tissue-specific annotations. As for coronary artery disease, heart-specific functional regions was highly enriched of GWAS signals, but previously identified risk loci were found to be most functional in other tissues, suggesting a substantial proportion of still undetected heart-related loci. In summary, GenoSkyline annotations can guide genetic studies at multiple resolutions and provide valuable insights in understanding complex diseases. GenoSkyline is available at http://genocanyon.med.yale.edu/GenoSkyline.

An RNA-Seq-based approach to understanding the immune system of the american lobster (Homarus americanus)

The growth and accessibility of next-generation sequencing technologies has significantly enhanced our ability to determine the molecular mechanisms responsible for complex physiological processes in nonmodel organisms like crustaceans. The power and limitations of this technology are in the huge amount of data that is generated and the bioinformatic annotation of this data. Proper annotation is fundamental for understanding the molecular basis of a phenotype or condition. The American lobster (Homarus americanus) fishery is essential to the economic well-being of hundreds of small coastal Canadian and American communities. The long-term health and sustainability of this fishery is an important area of study. In addition, it is an excellent model for comparative physiological studies with other crab and shrimp species which are the basis of global aquaculture production. Understanding of decapod immune systems is critical as the most significant factor impacting sustainability and expansion of global shrimp aquaculture is disease. This study will use RNA-Seq studies of the American lobster to demonstrate an approach that significantly enhances immune-related annotation of genes involved in the crustacean immune response. In addition, it will highlight the similarities and differences between the lobster humoral immune response to Gram-positive bacterial (Aerococcus viridans var. homari) and scuticociliate (Anophryoides haemophila) challenges.

Image Retrieval: Modelling Keywords via Low-level Features

Image Retrieval: Modelling Keywords via Low-level Features