Homology-based Approach Research Articles

Genome-wide selection of potential target candidates for RNAi against Nilaparvata lugens

BackgroundNilaparvata lugens is one of the most destructive pests of rice. RNAi-based N. lugens control offers one alternative strategy to traditional chemical insecticides. However, selection of potential target for RNAi against N. lugens remains a major challenge. Only two target genes for nuclear transgenic N. lugens-resistant plants have been screened. Importantly, only one or few potential target genes against N. lugens were screened every time by knowledge of essential genes from model organisms in previous study.ResultsHere, in silico genome-wide selection of potential target genes against N. lugens through homology comparison was performed. Through genome synteny comparisons, about 3.5% of Drosophila melanogaster genome was found to have conserved genomic synteny with N. lugens genome. By using N. lugens proteins to search D. melanogaster homologs defining lethal or sterile phenotype, 358 N. lugens genes were first screened as putative target genes. Transgenic rice lines expressing dsRNA of randomly selected gene (NlRan or NlSRP54) from 358 putative target genes enhanced resistance to N. lugens. After expression check and safety check, 115 N. lugens genes were screened as potential target candidates.ConclusionThe combined efforts in this study firstly provide one in silico genome-wide homology-based screening approach for RNAi-based target genes against N. lugens, which not only offer one new opportunity to batch select potential target candidates in pests of interest, but also will facilitate the selection of RNAi target in many pest species by providing more than one hundred potential target candidates.

Assembling bacterial puzzles: piecing together functions into microbial pathways.

Functional metagenomics enables the study of unexplored bacterial diversity, gene families, and pathways essential to microbial communities. However, discovering biological insights with these data is impeded by the scarcity of quality annotations. Here, we use a co-occurrence-based analysis of predicted microbial protein functions to uncover pathways in genomic and metagenomic biological systems. Our approach, based on phylogenetic profiles, improves the identification of functional relationships, or participation in the same biochemical pathway, between enzymes over a comparable homology-based approach. We optimized the design of our profiles to identify potential pathways using minimal data, clustered functionally related enzyme pairs into multi-enzymatic pathways, and evaluated our predictions against reference pathways in the KEGG database. We then demonstrated a novel extension of this approach to predict inter-bacterial protein interactions amongst members of a marine microbiome. Most significantly, we show our method predicts emergent biochemical pathways between known and unknown functions. Thus, our work establishes a basis for identifying the potential functional capacities of the entire metagenome, capturing previously unknown and abstract functions into discrete putative pathways.

Identification of tomato F-box proteins functioning in phenylpropanoid metabolism.

Phenylpropanoids, a class of specialized metabolites, play crucial roles in plant growth and stress adaptation and include diverse phenolic compounds such as flavonoids. Phenylalanine ammonia-lyase (PAL) and chalcone synthase (CHS) are essential enzymes functioning at the entry points of general phenylpropanoid biosynthesis and flavonoid biosynthesis, respectively. In Arabidopsis, PAL and CHS are turned over through ubiquitination-dependent proteasomal degradation. Specific kelch domain-containing F-Box (KFB) proteins as components of ubiquitin E3 ligase directly interact with PAL or CHS, leading to polyubiquitinated PAL and CHS, which in turn influences phenylpropanoid and flavonoid production. Although phenylpropanoids are vital for tomato nutritional value and stress responses, the post-translational regulation of PAL and CHS in tomato remains unknown. We identified 31 putative KFB-encoding genes in the tomato genome. Our homology analysis and phylogenetic study predicted four PAL-interacting SlKFBs, while SlKFB18 was identified as the sole candidate for the CHS-interacting KFB. Consistent with their homolog function, the predicted four PAL-interacting SlKFBs function in PAL degradation. Surprisingly, SlKFB18 did not interact with tomato CHS and the overexpression or knocking out of SlKFB18 did not affect phenylpropanoid contents in tomato transgenic lines, suggesting its irreverence with flavonoid metabolism. Our study successfully discovered the post-translational regulatory machinery of PALs in tomato while highlighting the limitation of relying solely on a homology-based approach to predict interacting partners of F-box proteins.

Annotations of four high-quality indigenous chicken genomes identify more than one thousand missing genes in subtelomeric regions and micro-chromosomes with high G/C contents.

Although multiple chicken genomes have been assembled and annotated, the numbers of protein-coding genes in chicken genomes and their variation among breeds are still uncertain due to the low quality of these genome assemblies and limited resources used in their gene annotations. To fill these gaps, we recently assembled genomes of four indigenous chicken breeds with distinct traits at chromosome-level. In this study, we annotated genes in each of these assembled genomes using a combination of RNA-seq- and homology-based approaches. We identified varying numbers (17,497-17,718) of protein-coding genes in the four indigenous chicken genomes, while recovering 51 of the 274 "missing" genes in birds in general, and 36 of the 174 "missing" genes in chickens in particular. Intriguingly, based on deeply sequenced RNA-seq data collected in multiple tissues in the four breeds, we found 571 ~ 627 protein-coding genes in each genome, which were missing in the annotations of the reference chicken genomes (GRCg6a and GRCg7b/w). After removing redundancy, we ended up with a total of 1,420 newly annotated genes (NAGs). The NAGs tend to be found in subtelomeric regions of macro-chromosomes (chr1 to chr5, plus chrZ) and middle chromosomes (chr6 to chr13, plus chrW), as well as in micro-chromosomes (chr14 to chr39) and unplaced contigs, where G/C contents are high. Moreover, the NAGs have elevated quadruplexes G frequencies, while both G/C contents and quadruplexes G frequencies in their surrounding regions are also high. The NAGs showed tissue-specific expression, and we were able to verify 39 (92.9%) of 42 randomly selected ones in various tissues of the four chicken breeds using RT-qPCR experiments. Most of the NAGs were also encoded in the reference chicken genomes, thus, these genomes might harbor more genes than previously thought. The NAGs are widely distributed in wild, indigenous and commercial chickens, and they might play critical roles in chicken physiology. Counting these new genes, chicken genomes harbor more genes than originally thought.

Identification of micronutrient deficiency related miRNA and their targets in Triticum aestivum using bioinformatics approach

Identification of miRNAs and their target proteins infer their functions to understand the biological processes of miRNAs and their involvement in plant growth and development. The homology-based approach (BLAST suite) was used for the identification of miRNA related to Zn and Cu deficiency in the bread wheat genome. Calculated the coding potential for the precursor miRNA and then predicted their secondary structure through RNAfold. PmiREN online server identified the miRNA target wheat protein. Further, STRING database predicted the biological relevance of the target protein. This in-silico study has identified the 3 miRNAs of the respective family of miR528, miR397, and miR168 of Triticum aestivum related to Cu and Zn deficiency. Out of the 42 targets for tae-miR397c; one of the targets is MFS domain-containing protein that contributes to “electron transfer” between photosystem700 and the “cytochrome b6-f complex” in photosystem and rest of the targets are laccase protein; involved in cell wall ligning deposition. Tae-miR528c has 6 targets; four are uncategorized proteins and the remaining two targets viz. GRF-type domain-containing protein and phytocyanin domain-containing protein are responsible for Zn ion binding and participate in electron transfer activity. The protein-protein interactions (PPIs) have found the various proteins that are associated with these identified miRNAs (tae-miR397c and tae-miR528c) target the protein that could be annotated further for their role in plant growth and development. The current computational hypothesis has developed a fast and robust pipeline to identify plant miRNAs and their targets compared to other used approaches.

PH-shape: an adaptive persistent homology-based approach for building outline extraction from ALS point cloud data

ABSTRACT Building outline extraction from segmented point clouds is a critical step of building footprint generation. Existing methods for this task are often based on the convex hull and α-shape algorithm. There are also some methods using grids and Delaunay triangulation. The common challenge of these methods is the determination of proper parameters. While deep learning-based methods have shown promise in reducing the impact and dependence on parameter selection, their reliance on datasets with ground truth information limits the generalization of these methods. In this study, a novel unsupervised approach, called PH-shape, is proposed to address the aforementioned challenge. The methods of Persistence Homology (PH) and Fourier descriptor are introduced into the task of building outline extraction. The PH from the theory of topological data analysis supports the automatic and adaptive determination of proper buffer radius, thus enabling the parameter-adaptive extraction of building outlines through buffering and “inverse” buffering. The quantitative and qualitative experiment results on two datasets with different point densities demonstrate the effectiveness of the proposed approach in the face of various building types, interior boundaries, and the density variation in the point cloud data of one building. The PH-supported parameter adaptivity helps the proposed approach overcome the challenge of parameter determination and data variations and achieve reliable extraction of building outlines.

Genome-Wide Characterization of Fennel (Anethum foeniculum) MiRNome and Identification of its Potential Targets in Homo sapiens and Arabidopsis thaliana: An Inter and Intra-species Computational Scrutiny.

MicroRNAs could be promising biomarkers for various diseases, and small RNA drugs have already been FDA approved for clinical use. This area of research is rapidly expanding and has significant potential for the future. Fennel (Anethum foeniculum) is a highly esteemed spice plant with economic and medicinal benefits, making it an invaluable asset in the pharmaceutical industry. To characterize the fennel miRNAs and their Arabidopsis thaliana and Homo sapience targets with functional enrichment analysis and human disease association. A homology-based computational approach characterized the MiRnome of the Anethum foeniculumgenome and assessed its impact on Arabidopsis thaliana andHomo sapience transcriptomes. In addition, functional enrichment analysis was evaluated for both species' targets. Moreover, PPI network analysis, hub gene identification, and MD simulation analysis of the top hub node with fennel miRNA were incorporated. We have identified 100 miRNAs of fennel and their target genes, which include 2536 genes in Homo sapiens and 1314 genes in Arabidopsis thaliana. Functional enrichment analysis reveals 56 Arabidopsis thaliana targets of fennel miRNAs showed involvement in metabolic pathways. Highly enriched human KEGG pathways were associated with several diseases, especially cancer. The protein-protein interaction network of human targets determined the top ten nodes; from them, seven hub nodes, namely MAPK1, PIK3R1, STAT3, EGFR, KRAS, CDC42, and SMAD4, have shown their involvement in the pancreatic cancer pathway. Based on the Blast algorithm, 21 fennel miRNAs are homologs to 16 human miRNAs were predicted; from them, the CSPP1 target was a common target for afo-miR11117a-3p and has-miR-6880-5p homologs miRNAs. Our results are the first to report the 100 fennel miRNAs, and predictions for their endogenous and human target genes provide a basis for further understanding of Anethum foeniculum miRNAs and the biological processes and diseases with which they are associated.

Unraveling the Polysaccharide Biosynthesis Potential of Ganoderma lucidum: A Chromosome-Level Assembly Using Hi-C Sequencing.

Ganoderma lucidum exhibits the ability to synthesize a diverse range of biologically active molecules with significant pharmaceutical potential, including xylomannan and fucogalactan, which have demonstrated antitumor activity. However, there exists considerable intra-species variability in the capacity to produce these metabolites at high concentrations, likely reflecting the high genomic diversity observed from a limited number of strains sequenced to date. We employed high-throughput shotgun sequencing to obtain the complete genome sequence of G. lucidum strain 5.1, which is distinguished by its remarkable xylomannan synthesis capabilities. Through the utilization of semi-automatic reordering based on conformation capture (Hi-C) data, we substantially enhanced the assembly process, resulting in the generation of 12 chromosome-level scaffolds with a cumulative length of 39 Mbp. By employing both de novo and homology-based approaches, we performed comprehensive annotation of the genome, thereby identifying a diverse repertoire of genes likely involved in polysaccharide biosynthesis. The genome sequence generated in this study serves as a valuable resource for elucidating the molecular mechanisms underlying the medicinal potential of Ganoderma species, discovering novel pharmaceutically valuable compounds, and elucidating the ecological mechanisms of the species. Furthermore, the chromosome contact map obtained for the first time for this species extends our understanding of 3D fungal genomics and provides insights into the functional and structural organization within the fungal kingdom.

The Genome of the Korean Island-Originated Perilla citriodora 'Jeju17' Sheds Light on Its Environmental Adaptation and Fatty Acid and Lipid Production Pathways.

Perilla is a key component of Korean food. It contains several plant-specialized metabolites that provide medical benefits. In response to an increased interest in healthy supplement food from the public, people are focusing on the properties of Perilla. Nevertheless, unlike rice and soybeans, there are few studies based on molecular genetics on Perilla, so it is difficult to systematically study the molecular breed. The wild Perilla, Perilla citriodora 'Jeju17', was identified a decade ago on the Korean island of Jeju. Using short-reads, long-reads, and Hi-C, a chromosome-scale genome spanning 676 Mbp, with high contiguity, was assembled. Aligning the 'Jeju17' genome to the 'PC002' Chinese species revealed significant collinearity with respect to the total length. A total of 31,769 coding sequences were predicted, among which 3331 were 'Jeju17'-specific. Gene enrichment of the species-specific gene repertoire highlighted environment adaptation, fatty acid metabolism, and plant-specialized metabolite biosynthesis. Using a homology-based approach, genes involved in fatty acid and lipid triacylglycerol biosynthesis were identified. A total of 22 fatty acid desaturases were found and comprehensively characterized. Expression of the FAD genes in 'Jeju17' was examined at the seed level, and hormone signaling factors were identified. The results showed that the expression of FAD genes in 'Jeju17' at the seed level was high 25 days after flowering, and their responses of hormones and stress were mainly associated with hormone signal transduction and abiotic stress via cis-elements patterns. This study presents a chromosome-level genome assembly of P. citriodora 'Jeju17', the first wild Perilla to be sequenced from the Korean island of Jeju. The analyses provided can be useful in designing ALA-enhanced Perilla genotypes in the future.

Uncovering new families and folds in the natural protein universe

We are now entering a new era in protein sequence and structure annotation, with hundreds of millions of predicted protein structures made available through the AlphaFold database1. These models cover nearly all proteins that are known, including those challenging to annotate for function or putative biological role using standard homology-based approaches. In this study, we examine the extent to which the AlphaFold database has structurally illuminated this ‘dark matter’ of the natural protein universe at high predicted accuracy. We further describe the protein diversity that these models cover as an annotated interactive sequence similarity network, accessible at https://uniprot3d.org/atlas/AFDB90v4. By searching for novelties from sequence, structure and semantic perspectives, we uncovered the β-flower fold, added several protein families to Pfam database2 and experimentally demonstrated that one of these belongs to a new superfamily of translation-targeting toxin–antitoxin systems, TumE–TumA. This work underscores the value of large-scale efforts in identifying, annotating and prioritizing new protein families. By leveraging the recent deep learning revolution in protein bioinformatics, we can now shed light into uncharted areas of the protein universe at an unprecedented scale, paving the way to innovations in life sciences and biotechnology.

Novel integrated computational AMP discovery approaches highlight diversity in the helminth AMP repertoire.

Antimicrobial Peptides (AMPs) are immune effectors that are key components of the invertebrate innate immune system providing protection against pathogenic microbes. Parasitic helminths (phylum Nematoda and phylum Platyhelminthes) share complex interactions with their hosts and closely associated microbiota that are likely regulated by a diverse portfolio of antimicrobial immune effectors including AMPs. Knowledge of helminth AMPs has largely been derived from nematodes, whereas the flatworm AMP repertoire has not been described. This study highlights limitations in the homology-based approaches, used to identify putative nematode AMPs, for the characterisation of flatworm AMPs, and reveals that innovative algorithmic AMP prediction approaches provide an alternative strategy for novel helminth AMP discovery. The data presented here: (i) reveal that flatworms do not encode traditional lophotrochozoan AMP groups (Big Defensin, CSαβ peptides and Myticalin); (ii) describe a unique integrated computational pipeline for the discovery of novel helminth AMPs; (iii) reveal >16,000 putative AMP-like peptides across 127 helminth species; (iv) highlight that cysteine-rich peptides dominate helminth AMP-like peptide profiles; (v) uncover eight novel helminth AMP-like peptides with diverse antibacterial activities, and (vi) demonstrate the detection of AMP-like peptides from Ascaris suum biofluid. These data represent a significant advance in our understanding of the putative helminth AMP repertoire and underscore a potential untapped source of antimicrobial diversity which may provide opportunities for the discovery of novel antimicrobials. Further, unravelling the role of endogenous worm-derived antimicrobials and their potential to influence host-worm-microbiome interactions may be exploited for the development of unique helminth control approaches.

OsFAR1 is involved in primary fatty alcohol biosynthesis and promotes drought tolerance in rice.

OsFAR1 encodes a fatty acyl-CoA reductase involved in biosynthesis of primary alcohols and plays an important role in drought stress response in rice. Cuticular waxes cover the outermost surface of terrestrial plants and contribute to inhibiting nonstomatal water loss and improving plant drought resistance. Primary alcohols are the most abundant components in the leaf cuticular waxes of rice (Oryza sativa), but the biosynthesis and regulation of primary alcohol remain largely unknown in rice. Here, we identified and characterized an OsFAR1 gene belonging to the fatty acyl-CoA reductases (FARs) via a homology-based approach in rice. OsFAR1 was activated by abiotic stresses and abscisic acid, resulting in increased production of primary alcohol in rice. Heterologous expression of OsFAR1 enhanced the amounts of C22:0 and C24:0 primary alcohols in yeast (Saccharomyces cerevisiae) and C24:0 to C32:0 primary alcohols in Arabidopsis. Similarly, OsFAR1 overexpression significantly increased the content of C24:0 to C30:0 primary alcohols on rice leaves. Finally, OsFAR1 overexpression lines exhibited reduced cuticle permeability and enhanced drought tolerance in rice and Arabidopsis. Taken together, our results demonstrate that OsFAR1 is involved in rice primary alcohol biosynthesis and plays an important role in responding to drought and other environmental stresses.

INSILICO ANALYSIS OF FUNCTIONAL SNPs IN HUMAN CRB2 GENE ASSOCIATED WITH FOCAL SEGMENTAL GLOMERULOSCLEROSIS

The CRB2 gene contributes to the onset of hereditary focal segmental glomerulosclerosis, which damages the kidney, particularly the glomerulus, and results in chronic kidney failure. Missense variations account for more than 90% of mutations that alter the protein's structure or activities. The pathogenicity of CRB2 non-synonymous single nucleotide polymorphisms (nsSNPs) was predicted in this work using a variety of bioinformatics techniques for mutation analysis. We retrieved 1201 nsSNPs from dbSNP-NCBI for the analysis, and 20 were predicted deleterious. 20 missense variants are G349D, C629S, R534W, G178D, C620Y, C620S, R628C, R633G, R633W, E643A, T841M, R960S, R960C, P1064T, P1064S, N800K, G1088D, T1187P, R1249Q, and R1249P. SIFT, PROVEAN, Mutation Assessor, and PANTHER, four sequence homology-based approaches, suggested that one variation, R960S, was benign and that 19 SNPS were deleterious. Six supervised based approaches, including SNAP2, MutPred2, SuSPect, PhD-SNP, SNPs&Go, and PMut, predicted 14 SNPs as detrimental in all six applied methods. 13 variations were identified as harmful by one structure-based technique, Poly-Phen, and two consensus-based methods, Meta-SNP and Predict-SNP. The CRB2 protein's mutant and wild-type structures were predicted using itaser. Following PyMol 5 structural analysis, the mutations rs879255250 Cysteine at 620 changes to Tyrosine, variation rs879255250 Glycine at 178 changes to Aspartate, and variant rs1322315181 Glycine at 178 changes to Aspartate Arginine changes to Proline at position 1249, and Cysteine changes to Serine at position 620, with variants rs147412276 and rs868484209. Proline at position 1064 alterations to Serine are expected to be extremely harmful and result in structural changes in the protein, which may be candidates for the FSGS aetiology. These CBR2 nsSNPs may thus be possibilities for diagnostic genetic screening and therapeutic molecular targeting.
 

Deep embeddings to comprehend and visualize microbiome protein space

Understanding the function of microbial proteins is essential to reveal the clinical potential of the microbiome. The application of high-throughput sequencing technologies allows for fast and increasingly cheaper acquisition of data from microbial communities. However, many of the inferred protein sequences are novel and not catalogued, hence the possibility of predicting their function through conventional homology-based approaches is limited, which indicates the need for further research on alignment-free methods. Here, we leverage a deep-learning-based representation of proteins to assess its utility in alignment-free analysis of microbial proteins. We trained a language model on the Unified Human Gastrointestinal Protein catalogue and validated the resulting protein representation on the bacterial part of the SwissProt database. Finally, we present a use case on proteins involved in SCFA metabolism. Results indicate that the deep learning model manages to accurately represent features related to protein structure and function, allowing for alignment-free protein analyses. Technologies that contextualize metagenomic data are a promising direction to deeply understand the microbiome.

The Rad9-Rad1-Hus1 DNA Repair Clamp is Found in Microsporidia.

DNA repair is an important component of genome integrity and organisms with reduced repair capabilities tend to accumulate mutations at elevated rates. Microsporidia are intracellular parasites exhibiting high levels of genetic divergence postulated to originate from the lack of several proteins, including the heterotrimeric Rad9–Rad1–Hus1 DNA repair clamp. Microsporidian species from the Encephalitozoonidae have undergone severe streamlining with small genomes coding for about 2,000 proteins. The highly divergent sequences found in Microsporidia render functional inferences difficult such that roughly half of these 2,000 proteins have no known function. Using a structural homology-based annotation approach combining protein structure prediction and tridimensional similarity searches, we found that the Rad9–Rad1–Hus1 DNA clamp is present in Microsporidia, together with many other components of the DNA repair machinery previously thought to be missing from these organisms. Altogether, our results indicate that the DNA repair machinery is present and likely functional in Microsporidia.

Functional dissection of human mitotic genes using CRISPR-Cas9 tiling screens.

The identity of human protein-coding genes is well known, yet our in-depth knowledge of their molecular functions and domain architecture remains limited by shortcomings in homology-based predictions and experimental approaches focused on whole-gene depletion. To bridge this knowledge gap, we developed a method that leverages CRISPR-Cas9-induced mutations across protein-coding genes for the a priori identification of functional regions at the sequence level. As a test case, we applied this method to 48 human mitotic genes, revealing hundreds of regions required for cell proliferation, including domains that were experimentally characterized, ones that were predicted based on homology, and novel ones. We validated screen outcomes for 15 regions, including amino acids 387-402 of Mad1, which were previously uncharacterized but contribute to Mad1 kinetochore localization and chromosome segregation fidelity. Altogether, we demonstrate that CRISPR-Cas9-based tiling mutagenesis identifies key functional domains in protein-coding genes de novo, which elucidates separation of function mutants and allows functional annotation across the human proteome.

A deep learning model to detect novel pore-forming proteins

Many pore-forming proteins originating from pathogenic bacteria are toxic against agricultural pests. They are the key ingredients in several pesticidal products for agricultural use, including transgenic crops. There is an urgent need to identify novel pore-forming proteins to combat development of resistance in pests to existing products, and to develop products that are effective against a broader range of pests. Existing computational methodologies to search for these proteins rely on sequence homology-based approaches. These approaches are based on similarities between protein sequences, and thus are limited in their usefulness for discovering novel proteins. In this paper, we outline a novel deep learning model trained on pore-forming proteins from the public domain. We compare different ways of encoding protein information during training, and contrast it with traditional approaches. We show that our model is capable of identifying known pore formers with no sequence similarity to the proteins used to train the model, and therefore holds promise for identifying novel pore formers.

Molecular characterization and differential expression of an aromatic heptaketide producing type III plant polyketide synthase from Himalayan rhubarb

Rheum australe (Himalayan Rhubarb, Polygonaceae), an endangered medicinal and vegetable herb owes its age-old remedying properties to the bio-active phyto-constituents viz. anthraquinones, stilbenoids, chromones and dietary flavonoids. Polyketide pathway primarily involving the intricate Type III polyketide synthases (PKSs) contributes to the biosynthesis of these phyto-constituents. In the present study, we perform a homology-based approach to isolate an 1176 bp full-length cds sequence of the RaALS gene showing an equitable level of sequence similarity to related Type III PKSs at both nucleic acid and amino acid levels. In silico characterization revealed the presence of highly conserved amino acid residues found in nearly all Type III PKSs including the conserved active-site residues, signature motif and cyclization pocket residues with an exception of Ile256 and Gly258. Docking studies established major interactions between the starter acetyl-CoA and RaALS. Copy number analysis suggested slender evolution in Type III PKS in R. australe having a single copy of RaALS gene. qRT-PCR analyses revealed corroboration between the higher expression of RaALS in leaves followed by stem and root with that of the metabolite concentration. Expression studies further showed a direct increase of RaALS transcripts with the growing metabolite accretion in relation to altitude suggesting a probable involvement of specific Type III PKS in biosynthesis of the major phyto-constituents. Furthermore, abiotic stressors viz. methyl jasmonate, salicylic acid and UV light enhanced RaALS transcription hinting towards its role in defense mechanism in R. australe and highlighting the significance of RaALS as a prospective target for metabolic engineering.

Reconstruction of Genome-Scale Metabolic Model for Hansenula polymorpha Using RAVEN.

Genome-scale metabolic models (GEMs) provide a useful framework for modeling the metabolism of microorganisms. While the applications of GEMs are wide and far reaching, the reconstruction and continuous curation of such models can be perceived as a tedious and time-consuming task. Using RAVEN, a MATLAB-based toolbox designed to facilitate the reconstruction analysis of metabolic networks, this protocol practically demonstrates how researchers can create their own GEMs using a homology-based approach. To provide a complete example, a draft GEM for the industrially relevant yeast Hansenula polymorpha is reconstructed.

ReGSP: a visualized application for homology-based gene searching and plotting using multiple reference sequences.

The massively parallel nature of next-generation sequencing technologies has contributed to the generation of massive sequence data in the last two decades. Deciphering the meaning of each generated sequence requires multiple analysis tools, at all stages of analysis, from the reads stage all the way up to the whole-genome level. Homology-based approaches based on related reference sequences are usually the preferred option for gene and transcript prediction in newly sequenced genomes, resulting in the popularity of a variety of BLAST and BLAST-based tools. For organelle genomes, a single-reference–based gene finding tool that uses grouping parameters for BLAST results has been implemented in the Genome Search Plotter (GSP). However, this tool does not accept multiple and user-customized reference sequences required for a broad homology search. Here, we present multiple Reference–based Gene Search and Plot (ReGSP), a simple and convenient web tool that accepts multiple reference sequences for homology-based gene search. The tool incorporates cPlot, a novel dot plot tool, for illustrating nucleotide sequence similarity between the query and the reference sequences. ReGSP has an easy-to-use web interface and is freely accessible at https://ds.mju.ac.kr/regsp.