BackgroundThe cultivated tomato is second most consumed vegetable of the world and is an important part of a diverse and balanced diet as a rich source of vitamins, minerals, phenolic antioxidants and antioxidant lycopene having anti-cancer properties. To reap benefit of genomics of the domestic tomato (Solanum lycopersicum L.) unravelled by Tomato Genome Consortium (The Tomato Genome Consortium, 2012), the bulk mining of its markers in totality is imperative and critically required. The solgenomics has limited number of microsatellite DNA markers (2867) pertaining to solanaceae family. As these markers are of linkage map having relative distance, the choice of selected markers based on absolute distance as of physical map is missing. Only limited microsatellite markers with limitations are reported for variety identification thus there is a need for more markers supplementing DUS test and also for traceability of product in global market.DescriptionWe present here the first whole genome based microsatellite DNA marker database of tomato, TomSatDB (Tomato MicroSatellite Database) with more than 1.4 million markers mined in-silico, using MIcroSAtellite (MISA) tool. To cater the customized needs of wet lab, features with a novelty of an automated primer designing tool is added. TomSatDB (http://cabindb.iasri.res.in/tomsatdb), a user-friendly and freely accessible tool offers chromosome wise as well as location wise search of primers. It is an online relational database based on “three-tier architecture” that catalogues information of microsatellites in MySQL and user-friendly interface developed using PHP (Hypertext Pre Processor).ConclusionBesides abiotic stress, tomato is known to have biotic stress due to its susceptibility over 200 diseases caused by pathogenic fungi, bacteria, viruses and nematodes. These markers are expected to pave the way of germplasm management over abiotic and biotic stress as well as improvement through molecular breeding, leading to increased tomato productivity in India as well as other parts of the world. In era of IPR the new variety can be identified based on allelic variation among varieties supplementing DUS test and product traceability.

Molecular markers play a significant role for crop improvement in desirable characteristics, such as high yield, resistance to disease and others that will benefit the crop in long term. Pigeonpea (Cajanus cajan L.) is the recently sequenced legume by global consortium led by ICRISAT (Hyderabad, India) and been analysed for gene prediction, synteny maps, markers, etc. We present PIgeonPEa Microsatellite DataBase (PIPEMicroDB) with an automated primer designing tool for pigeonpea genome, based on chromosome wise as well as location wise search of primers. Total of 123 387 Short Tandem Repeats (STRs) were extracted from pigeonpea genome, available in public domain using MIcroSAtellite tool (MISA). The database is an online relational database based on ‘three-tier architecture’ that catalogues information of microsatellites in MySQL and user-friendly interface is developed using PHP. Search for STRs may be customized by limiting their location on chromosome as well as number of markers in that range. This is a novel approach and is not been implemented in any of the existing marker database. This database has been further appended with Primer3 for primer designing of selected markers with left and right flankings of size up to 500 bp. This will enable researchers to select markers of choice at desired interval over the chromosome. Furthermore, one can use individual STRs of a targeted region over chromosome to narrow down location of gene of interest or linked Quantitative Trait Loci (QTLs). Although it is an in silico approach, markers’ search based on characteristics and location of STRs is expected to be beneficial for researchers.Database URL: http://cabindb.iasri.res.in/pigeonpea/

BackgroundThough India has sequenced water buffalo genome but its draft assembly is based on cattle genome BTau 4.0, thus de novo chromosome wise assembly is a major pending issue for global community. The existing radiation hybrid of buffalo and these reported STR can be used further in final gap plugging and “finishing” expected in de novo genome assembly. QTL and gene mapping needs mining of putative STR from buffalo genome at equal interval on each and every chromosome. Such markers have potential role in improvement of desirable characteristics, such as high milk yields, resistance to diseases, high growth rate. The STR mining from whole genome and development of user friendly database is yet to be done to reap the benefit of whole genome sequence.DescriptionBy in silico microsatellite mining of whole genome, we have developed first STR database of water buffalo, BuffSatDb (Buffalo MicroSatellite Database (http://cabindb.iasri.res.in/buffsatdb/) which is a web based relational database of 910529 microsatellite markers, developed using PHP and MySQL database. Microsatellite markers have been generated using MIcroSAtellite tool. It is simple and systematic web based search for customised retrieval of chromosome wise and genome-wide microsatellites. Search has been enabled based on chromosomes, motif type (mono-hexa), repeat motif and repeat kind (simple and composite). The search may be customised by limiting location of STR on chromosome as well as number of markers in that range. This is a novel approach and not been implemented in any of the existing marker database. This database has been further appended with Primer3 for primer designing of the selected markers enabling researcher to select markers of choice at desired interval over the chromosome. The unique add-on of degenerate bases further helps in resolving presence of degenerate bases in current buffalo assembly.ConclusionBeing first buffalo STR database in the world , this would not only pave the way in resolving current assembly problem but shall be of immense use for global community in QTL/gene mapping critically required to increase knowledge in the endeavour to increase buffalo productivity, especially for third world country where rural economy is significantly dependent on buffalo productivity.

Wheat fulfills 20% of global caloric requirement. World needs 60% more wheat for 9 billion population by 2050 but climate change with increasing temperature is projected to affect wheat productivity adversely. Trait improvement and management of wheat germplasm requires genomic resource. Simple Sequence Repeats (SSRs) being highly polymorphic and ubiquitously distributed in the genome, can be a marker of choice but there is no structured marker database with options to generate primer pairs for genotyping on desired chromosome/physical location. Previously associated markers with different wheat trait are also not available in any database. Limitations of in vitro SSR discovery can be overcome by genome-wide in silico mining of SSR. Triticum aestivum SSR database (TaSSRDb) is an integrated online database with three-tier architecture, developed using PHP and MySQL and accessible at http://webtom.cabgrid.res.in/wheatssr/. For genotyping, Primer3 standalone code computes primers on user request. Chromosome-wise SSR calling for all the three sub genomes along with choice of motif types is provided in addition to the primer generation for desired marker. We report here a database of highest number of SSRs (476,169) from complex, hexaploid wheat genome (~17 GB) along with previously reported 268 SSR markers associated with 11 traits. Highest (116.93 SSRs/Mb) and lowest (74.57 SSRs/Mb) SSR densities were found on 2D and 3A chromosome, respectively. To obtain homozygous locus, e-PCR was done. Such 30 loci were randomly selected for PCR validation in panel of 18 wheat Advance Varietal Trial (AVT) lines. TaSSRDb can be a valuable genomic resource tool for linkage mapping, gene/QTL (Quantitative trait locus) discovery, diversity analysis, traceability and variety identification. Varietal specific profiling and differentiation can supplement DUS (Distinctiveness, Uniformity, and Stability) testing, EDV (Essentially Derived Variety)/IV (Initial Variety) disputes, seed purity and hybrid wheat testing. All these are required in germplasm management as well as also in the endeavor of wheat productivity.

Microbial diseases in fish, plant, animal and human are rising constantly; thus, discovery of their antidote is imperative. The use of antibiotic in aquaculture further compounds the problem by development of resistance and consequent consumer health risk by bio-magnification. Antimicrobial peptides (AMPs) have been highly promising as natural alternative to chemical antibiotics. Though AMPs are molecules of innate immune defense of all advance eukaryotic organisms, fish being heavily dependent on their innate immune defense has been a good source of AMPs with much wider applicability. Machine learning-based prediction method using wet laboratory-validated fish AMP can accelerate the AMP discovery using available fish genomic and proteomic data. Earlier AMP prediction servers are based on multi-phyla/species data, and we report here the world's first AMP prediction server in fishes. It is freely accessible at http://webapp.cabgrid.res.in/fishamp/ . A total of 151 AMPs related to fish collected from various databases and published literature were taken for this study. For model development and prediction, N-terminus residues, C-terminus residues and full sequences were considered. Best models were with kernels polynomial-2, linear and radial basis function with accuracy of 97, 99 and 97 %, respectively. We found that performance of support vector machine-based models is superior to artificial neural network. This in silico approach can drastically reduce the time and cost of AMP discovery. This accelerated discovery of lead AMP molecules having potential wider applications in diverse area like fish and human health as substitute of antibiotics, immunomodulator, antitumor, vaccine adjuvant and inactivator, and also for packaged food can be of much importance for industries.

Microsatellites are ubiquitously distributed, polymorphic repeat sequence valuable for association, selection, population structure and identification. They can be mined by genomic library, probe hybridization and sequencing of selected clones. Such approach has many limitations like biased hybridization and selection of larger repeats. In silico mining of polymorphic markers using data of various genotypes can be rapid and economical. Available tools lack in some or other aspects like: targeted user defined primer generation, polymorphism discovery using multiple sequence, size and number limits of input sequence, no option for primer generation and e-PCR evaluation, transferability, lack of complete automation and user-friendliness. They also lack the provision to evaluate published primers in e-PCR mode to generate additional allelic data using re-sequenced data of various genotypes for judicious utilization of previously generated data. We developed the tool (PolyMorphPredict) using Perl, R, Java and launched at Apache which is available at http://webtom.cabgrid.res.in/polypred/. It mines microsatellite loci and computes primers from genome/transcriptome data of any species. It can perform e-PCR using published primers for polymorphism discovery and across species transferability of microsatellite loci. Present tool has been evaluated using five species of different genome size having 21 genotypes. Though server is equipped with genomic data of three species for test run with gel simulation, but can be used for any species. Further, polymorphism predictability has been validated using in silico and in vitro PCR of four rice genotypes. This tool can accelerate the in silico microsatellite polymorphism discovery in re-sequencing projects of any species of plant and animal for their diversity estimation along with variety/breed identification, population structure, MAS, QTL and gene discovery, traceability, parentage testing, fungal diagnostics and genome finishing.

Heliothis virescens, a polyphagous pest, is one of the most destructive pests of many crops and vegetables. Various insecticides and pesticides are used by agriculturalists to stop the growth and development of this pest. RNA interference is a new area for the management of pests/insects by inhibiting the growth related RNAs. This involves the miRNAs identification and its characterization. In the present study, computational approach is applied to predict putative miRNA candidates along with their possible target(s) in the Heliothis virescens. A total of 63,662 ESTs were downloaded from dbEST database and processed, trimmed and masked through EGassembler. The H. virescens contigs database obtained after assembly was now used to find the putative miRNA candidates by performing a local BLAST with the miRNAs of insects retrieved from miRBase. We have predicted putative miRNA candidates by homology search against all the reported insect miRNAs. These putative miRNAs candidates were further validated and filtered by different features. In addition, we have also attempted to predict the putative targets of these filtered miRNAs, by making use of 3' untranslated regions of mRNAs from B. mori. These miRNAs and their targets in H. virescens will help in improved understanding of molecular mechanisms of miRNA and development of novel and more precise techniques for better understanding some post transcriptional gene silencing.

BackgroundIdentification of true to breed type animal for conservation purpose is imperative. Breed dilution is one of the major problems in sustainability except cases of commercial crossbreeding under controlled condition. Breed descriptor has been developed to identify breed but such descriptors cover only “pure breed” or true to the breed type animals excluding undefined or admixture population. Moreover, in case of semen, ova, embryo and breed product, the breed cannot be identified due to lack of visible phenotypic descriptors. Advent of molecular markers like microsatellite and SNP have revolutionized breed identification from even small biological tissue or germplasm. Microsatellite DNA marker based breed assignments has been reported in various domestic animals. Such methods have limitations viz. non availability of allele data in public domain, thus each time all reference breed has to be genotyped which is neither logical nor economical. Even if such data is available but computational methods needs expertise of data analysis and interpretation.ResultsWe found Bayesian Networks as best classifier with highest accuracy of 98.7% using 51850 reference allele data generated by 25 microsatellite loci on 22 goat breed population of India. The FST values in the study were seen to be low ranging from 0.051 to 0.297 and overall genetic differentiation of 13.8%, suggesting more number of loci needed for higher accuracy. We report here world’s first model webserver for breed identification using microsatellite DNA markers freely accessible at http://cabin.iasri.res.in/gomi/.ConclusionHigher number of loci is required due to less differentiable population and large number of breeds taken in this study. This server will reduce the cost with computational ease. This methodology can be a model for various other domestic animal species as a valuable tool for conservation and breed improvement programmes.