Batch Blast Extractor: an automated blastx parser application

Identification and functional prediction of lncRNAs associated with intramuscular lipid deposition in Guangling donkeys

Many studies have shown that long non-coding RNAs (lncRNAs) play key regulatory roles in various biological processes. However, the importance and molecular regulatory mechanisms of lncRNAs in donkey intramuscular fat deposition remain to be further investigated. In this study, we used published transcriptomic data from the longissimus dorsi muscle of Guangling donkeys to identify lncRNAs and obtained 196 novel lncRNAs. Compared with the coding genes, the novel lncRNAs and the known lncRNAs exhibited some typical features, such as shorter transcript length and smaller exons. A total of 272 coding genes and 52 lncRNAs were differentially expressed between the longissimus dorsi muscles of the low-fat and high-fat groups. The differentially expressed genes were found to be involved in various biological processes related to lipid metabolism. The potential target genes of differentially expressed lncRNAs were predicted by cis and trans. Functional analysis of lncRNA targets showed that some lncRNAs may act on potential target genes involved in lipid metabolism processes and regulate lipid deposition in the longissimus dorsi muscle. This study provides valuable information for further investigation of the molecular mechanisms of lipid deposition traits in donkeys, which may improve meat traits and facilitate the selection process of donkeys in future breeding.

Genomoviruses in Liver Samples of Molossus molossus Bats

CRESS-DNA encompasses a broad spectrum of viruses documented across diverse organisms such as animals, plants, diatoms, fungi, and marine invertebrates. Despite this prevalence, the full extent of these viruses' impact on the environment and their respective hosts remains incompletely understood. Furthermore, an increasing number of viruses within this category lack detailed characterization. This investigation focuses on unveiling and characterizing viruses affiliated with the Genomoviridae family identified in liver samples from the bat Molossus molossus. Leveraging viral metagenomics, we identified seven sequences (MmGmV-PA) featuring a circular DNA genome housing two ORFs encoding replication-associated protein (Rep) and capsid protein (Cap). Predictions based on conserved domains typical of the Genomoviridae family were established. Phylogenetic analysis revealed the segregation of these sequences into two clades aligning with the genera Gemycirculavirus (MmGmV-06-PA and MmGmV-07-PA) and Gemykibivirus (MmGmV-01-PA, MmGmV-02-PA, MmGmV-03-PA, MmGmV-05-PA, and MmGmV-09-PA). At the species level, pairwise comparisons based on complete nucleotide sequences indicated the potential existence of three novel species. In summary, our study significantly contributes to an enhanced understanding of the diversity of Genomoviridae within bat samples, shedding light on previously undiscovered viral entities and their potential ecological implications.

Integrative transcriptomic and metabolomic analyses reveal the phenylpropanoid and flavonoid biosynthesis of Prunus mume

Prunus mume is an important medicinal plant with ornamental and edible value. Its flowers contain phenylpropanoids, flavonoids and other active components, that have important medicinal and edible value, yet their molecular regulatory mechanisms in P. mume remain unclear. In this study, the content of total flavonoid and total phenylpropanoid of P. mume at different developmental periods was measured first, and the results showed that the content of total flavonoid and total phenylpropanoid gradually decreased in three developmental periods. Then, an integrated analysis of transcriptome and metabolome was conducted on three developmental periods of P. mume to investigate the law of synthetic accumulation for P. mume metabolites, and the key enzyme genes for the biosynthesis of phenylpropanoids and flavonoids were screened out according to the differentially expressed genes (DEGs). A total of 14,332 DEGs and 38 differentially accumulate metabolites (DAMs) were obtained by transcriptomics and metabolomics analysis. The key enzyme genes and metabolites in the bud (HL) were significantly different from those in the half-opening (BK) and full-opening (QK) periods. In the phenylpropanoid and flavonoid biosynthesis pathway, the ion abundance of chlorogenic acid, naringenin, kaempferol, isoquercitrin, rutin and other metabolites decreased with the development of flowers, while the ion abundance of cinnamic acid increased. Key enzyme genes such as HCT, CCR, COMT, CHS, F3H, and FLS positively regulate the downstream metabolites, while PAL, C4H, and 4CL negatively regulate the downstream metabolites. Moreover, the key genes FLS (CL4312-2, CL4312-3, CL4312-4, CL4312-5, CL4312-6) regulating the synthesis of flavonols are highly expressed in bud samples. The dynamic changes of these metabolites were validated by determining the content of 14 phenylpropanoids and flavonoids in P. mume at different developmental periods, and the transcription expression levels of these genes were validated by real-time PCR. Our study provides new insights into the molecular mechanism of phenylpropanoid and flavonoid accumulation in P. mume.

SMRT Sequencing Technology Was Used to Construct the Batocera horsfieldi (Hope) Transcriptome and Reveal Its Features

Batocera horsfieldi (Hope) (Coleoptera: Cerambycidae) is an important forest pest in China that mainly infests timber and economic forests. This pest primarily causes plant tissue to necrotize, rot, and eventually die by feeding on the woody parts of tree trunks. To gain a deeper understanding of the genetic mechanism of B. horsfieldi, this study employed single-molecule real-time sequencing (SMRT) and Illumina RNA-seq technologies to conduct full-length transcriptome sequencing of the insect. Total RNA extracted from male and female adults was mixed and subjected to SMRT sequencing, generating a complete transcriptome. Transcriptome analysis, prediction of long non-coding RNA (lncRNA), coding sequences (CDs), analysis of simple sequence repeats (SSR), prediction of transcription factors, and functional annotation of transcripts were performed in this study. The collective 20,356,793 subreads (38.26 G, clean reads) were generated, including 432,091 circular consensus sequences and 395,851 full-length non-chimera reads. The full-length non-chimera reads (FLNC) were clustered and redundancies were removed, resulting in 39,912 consensus reads. SSR and ANGEL software v3.0 were used for predicting SSR and CDs. In addition, four tools were used for annotating 6058 lncRNAs, identifying 636 transcription factors. Furthermore, a total of 84,650 transcripts were functionally annotated in seven different databases. This is the first time that the full-length transcriptome of B. horsfieldi has been obtained using SMRT sequencing. This provides an important foundation for investigating the gene regulation underlying the interaction between B. horsfieldi and its host plants through gene editing in the future and provides a scientific basis for the prevention and control of B. horsfieldi.

Genome-wide identification of long intergenic non-coding RNAs for Ralstonia solanacearum resistance in tomato (Solanum lycopersicum)

There is growing evidences indicating that long intergenic ncRNAs (lincRNAs) play key roles in plant development and stress responses. To research tomato lincRNA functions during the interaction between tomato and Ralstonia solanacearum, RNA-seq data of tomato plants inoculated with R. solanacearum was analyzed. In this study, 315 possible lincRNAs were identified from RNA-seq data. Then 23 differentially expressed lincRNAs between tomato plants inoculated with R. solanacearum and control were identified and a total of 171 possible target genes for these differentially expressed lincRNAs were predicted. Through GO and KEGG analysis, we found that lincRNA might be involved in jasmonic acid and ethylene signaling pathways to respond to tomato bacterial wilt infection. Furthermore, lincRNA may also be involved in regulating the expression of AGO protein. Subsequently, analysis of expression patterns between differentially expressed lincRNAs and adjacent mRNAs by qRT-PCR revealed that part of lincRNAs and their possible target genes exhibited positive correlation. Taken together, these results suggest that lincRNAs play potential roles in tomato against R. solanacearum infection and will provide fundamental information about the lincRNA-based plant defense mechanisms.

Genome-Wide Identification of Long Noncoding RNA and Their Potential Interactors in ISWI Mutants

Long non-coding RNAs (lncRNAs) have been identified as key regulators of gene expression and participate in many vital physiological processes. Chromatin remodeling, being an important epigenetic modification, has been identified in many biological activities as well. However, the regulatory mechanism of lncRNA in chromatin remodeling remains unclear. In order to characterize the genome-wide lncRNA expression and their potential interacting factors during this process in Drosophila, we investigated the expression pattern of lncRNAs and mRNAs based on the transcriptome analyses and found significant differences between lncRNAs and mRNAs. Then, we performed TSA-FISH experiments of candidate lncRNAs and their potential interactors that have different functions in Drosophila embryos to determine their expression pattern. In addition, we also analyzed the expression of transposable elements (TEs) and their interactors to explore their expression in ISWI mutants. Our results provide a new perspective for understanding the possible regulatory mechanism of lncRNAs and TEs as well as their targets in chromatin remodeling.

Comprehensive Transcriptome Analysis of Follicles from Two Stages of the Estrus Cycle of Two Breeds Reveals the Roles of Long Intergenic Non-Coding RNAs in Gilts

Simple Summary

This study provides new perspectives about the roles of lincRNAs in the estrus expression of gilts, which is correlated with ovarian steroid hormone and follicular development. Follicular tissues from two stages of the estrus cycle of Large White and Mi gilts were used for RNA-seq. Some genes and lincRNAs related to estrus expression in pigs were discovered. PPI and ceRNA networks related to the estrus expression were constructed. These results suggest that the estrus expression may be affected by lincRNAs and their target genes.

Abstract

Visible and long-lasting estrus expression of gilts and sows effectively sends a mating signal. To reveal the roles of Long Intergenic Non-coding RNAs (lincRNAs) in estrus expression, RNA-seq was used to investigate the lincRNAs expression of follicular tissues from Large White gilts at diestrus (LD) and estrus (LE), and Chinese Mi gilts at diestrus (MD) and estrus (ME). Seventy-three differentially expressed lincRNAs (DELs) were found in all comparisons (LE vs. ME, LD vs. LE, and MD vs. ME comparisons). Eleven lincRNAs were differentially expressed in both LD vs. LE and MD vs. ME comparisons. Fifteen DELs were mapped onto the pig corpus luteum number Quantitative Trait Loci (QTL) fragments. A protein–protein interaction (PPI) network that involved estrus expression using 20 DEGs was then constructed. Interestingly, three predicted target DEGs (PTGs) (CYP19A1 of MSTRG.10910, CDK1 of MSTRG.10910 and MSTRG.23984, SCARB1 of MSTRG.1559) were observed in the PPI network. A competitive endogenous RNA (ceRNA) network including three lincRNAs, five miRNAs, and five genes was constructed. Our study provides new insight into the lincRNAs associated with estrus expression and follicular development in gilts.

Identification of Long Non-Coding RNAs Involved in Porcine Fat Deposition Using Two High-Throughput Sequencing Methods

Adipose is an important body tissue in pigs, and fatty traits are critical in pig production. The function of long non-coding RNA (lncRNA) in fat deposition and metabolism has been found in previous studies. In this study, we collected the adipose tissue of six Landrace pigs with contrast backfat thickness (n_high = 3, n_low = 3), after which we performed strand-specific RNA sequencing (RNA-seq) based on pooling and biological replicate methods. Biological replicate and pooling RNA-seq revealed 1870 and 1618 lncRNAs, respectively. Using edgeR, we determined that 1512 genes and 220 lncRNAs, 2240 genes and 127 lncRNAs were differentially expressed in biological replicate and pooling RNA-seq, respectively. After target gene prediction, we found that ACSL3 was cis-targeted by lncRNA TCONS-00052400 and could activate the conversion of long-chain fatty acids. In addition, lncRNA TCONS_00041740 cis-regulated gene ACACB regulated the rate-limiting enzyme in fatty acid oxidation. Since these genes have necessary functions in fat metabolism, the results imply that the lncRNAs detected in our study may affect backfat deposition in swine through regulation of their target genes. Our study explored the regulation of lncRNA and their target genes in porcine backfat deposition and provided new insights for further investigation of the biological functions of lncRNA.

Analysis of long intergenic non-coding RNAs transcriptomic profiling in skeletal muscle growth during porcine embryonic development

Skeletal muscle growth plays a critical role during porcine muscle development stages. Genome-wide transcriptome analysis reveals that long intergenic non-coding RNAs (lincRNAs) are implicated as crucial regulator involving in epigenetic regulation. However, comprehensive analysis of lincRNAs in embryonic muscle development stages remain still elusive. Here, we investigated the transcriptome profiles of Duroc embryonic muscle tissues from days 33, 65, and 90 of gestation using RNA-seq, and 228 putative lincRNAs were identified. Moreover, these lincRNAs exhibit the characteristics of shorter transcripts length, longer exons, less exon numbers and lower expression level compared with protein-coding transcripts. Expression profile analysis showed that a total of 120 lincRNAs and 2638 mRNAs were differentially expressed. In addition, we also performed quantitative trait locus (QTL) mapping analysis for differentially expressed lincRNAs (DE lincRNAs), 113 of 120 DE lincRNAs were localized on 2200 QTLs, we observed many QTLs involved in growth and meat quality traits. Furthermore, we predicted potential target genes of DE lincRNAs in cis or trans regulation. Gene ontology and pathway analysis reveals that potential targets of DE lincRNAs mostly were enriched in the processes and pathways related to tissue development, MAPK signaling pathway, Wnt signaling pathway, TGF-beta signaling pathway and insulin signaling pathway, which involved in skeletal muscle physiological functions. Based on cluster analysis, co-expression network analysis of DE lincRNAs and their potential target genes indicated that DE lincRNAs highly regulated protein-coding genes associated with skeletal muscle development. In this study, many of the DE lincRNAs may play essential roles in pig muscle growth and muscle mass. Our study provides crucial information for further exploring the molecular mechanisms of lincRNAs during skeletal muscle development.

Transcriptome Analysis Reveals Long Intergenic Non-Coding RNAs Contributed to Intramuscular Fat Content Differences between Yorkshire and Wei Pigs

Intramuscular fat (IMF) content is closely related to various meat traits, such as tenderness, juiciness, and flavor. The IMF content varies considerably among pig breeds with different genetic backgrounds. Long intergenic non-coding RNAs (lincRNAs) have been widely identified in many species and found to be an important class of regulators that can participate in multiple biological processes. However, the mechanism behind lincRNAs regulation of pig IMF content remains unknown and requires further study. In our study, we identified a total of 156 lincRNAs in the longissimus dorsi muscle of Wei (fat-type) and Yorkshire (lean-type) pigs using previously published data. These identified lincRNAs have shorter transcript length, longer exon length, lower exon number, and lower expression level as compared with protein-coding transcripts. We predicted potential target genes (PTGs) that are potentially regulated by lincRNAs in cis or trans regulation. Gene ontology and pathway analyses indicated that many potential lincRNAs target genes are involved in IMF-related processes or pathways, such as fatty acid catabolic process and adipocytokine signaling pathway. In addition, we analyzed quantitative trait locus (QTL) sites that differentially expressed lincRNAs (DE lincRNAs) between Wei and Yorkshire pigs co-localized. The QTL sites where DE lincRNAs co-localize are mostly related to IMF content. Furthermore, we constructed a co-expressed network between DE lincRNAs and their differentially expressed PTGs (DEPTGs). On the basis of their expression levels, we suggest that many DE lincRNAs can affect IMF development by positively or negatively regulating their PTGs. This study identified and analyzed some lincRNAs- and PTGs-related IMF development of the two pig breeds and provided new insight into research on the roles of lincRNAs in the two types of breeds.

Transcriptome Analysis Reveals the Effect of Long Intergenic Noncoding RNAs on Pig Muscle Growth and Fat Deposition

Muscle growth and fat deposition are the two important biological processes in the development of pigs which are closely related to the pig production performance. Long intergenic noncoding RNAs (lincRNAs), with lack of coding potential and the length of at least 200nt, have been extensively studied to play important roles in many biological processes. However, the importance and molecular regulation mechanism of lincRNAs in the process of muscle growth and fat deposition in pigs are still to be further studied comprehensively. In our study, we used the data, including liver, abdominal fat, and longissimus dorsi muscle of 240 days' age of two F2 full-sib female individuals from the white Duroc and Erhualian crossbreed, to identify 581 putative lincRNAs associated with pig muscle growth and fat deposition. The 581 putative lincRNAs shared many common features with other mammalian lincRNAs, such as fewer exons, lower expression levels, and shorter transcript lengths. Cross-tissue comparisons showed that many transcripts were tissue-specific and were involved in the important biological processes in their corresponding tissues. Gene ontology and pathway analysis revealed that many potential target genes (PTGs) of putative lincRNAs were involved in pig muscle growth and fat deposition-related processes, including muscle cell proliferation, lipid metabolism, and fatty acid degradation. In Quantitative Trait Locus (QTLs) analysis, some PTGs were screened from putative lincRNAs, MRPL12 is associated with muscle growth, GCGR and SLC25A10 were associated with fat deposition, and PPP3CA, DPYD, and FGGY were related not only to muscle growth but also to fat deposition. Therefore, it implied that these lincRNAs might participate in the biological processes related to muscle growth or fat deposition through homeostatic regulation of PTGs, but the detailed molecular regulatory mechanisms still needed to be further explored. This study lays the molecular foundation for the in-depth study of the role of lincRNAs in the pig muscle growth and fat deposition and further provides the new molecular markers for understanding the complex biological mechanisms of pig muscle growth and fat deposition.

Transcriptome Analysis Suggests the Roles of Long Intergenic Non-coding RNAs in the Growth Performance of Weaned Piglets

Long intergenic non-coding RNAs (lincRNAs) have been considered to play a key regulatory role in various biological processes. An increasing number of studies have utilized transcriptome analysis to obtain lincRNAs with functions related to cancer, but lincRNAs affecting growth rates in weaned piglets are rarely described. Although lincRNAs have been systematically identified in various mouse tissues and cell lines, studies of lincRNA in pigs remain rare. Therefore, identifying and characterizing novel lincRNAs affecting the growth performance of weaned piglets is of great importance. Here, we reconstructed 101,988 lincRNA transcripts and identified 1,078 lincRNAs in two groups of longissimus dorsi muscle (LDM) and subcutaneous fat (SF) based on published RNA-seq datasets. These lincRNAs exhibit typical characteristics, such as shorter lengths and lower expression relative to protein-encoding genes. Gene ontology analysis revealed that some lincRNAs could be involved in weaned piglet related processes, such as insulin resistance and the AMPK signaling pathway. We also compared the positional relationship between differentially expressed lincRNAs (DELs) and quantitative trait loci (QTL) and found that some of DELs may play an important role in piglet growth and development. Our work details part of the lincRNAs that may affect the growth performance of weaned piglets and promotes future studies of lincRNAs for molecular-assisted development in weaned piglets.

An integrated approach of bioassay and molecular docking to study the dihydroxylation mechanism of pyrene by naphthalene dioxygenase in Rhodococcus sp. ustb-1

Naphthalene dioxygenase (NDO) is the initial enzyme catalyzing the biodegradation of aromatic compounds, and it plays a key role in microbial remediation of polluting sites. In this study, Rhodococcus sp. ustb-1 derived from crude oil was selected to investigate the biodegradation characters and dihydroxylation mechanism of pyrene by an integrated approach of bioassay and molecular docking. The biodegradation experiment proved that the strain ustb-1 shows high effective biodegradability to pyrene with a 70.8% degradation on the 28th day and the metabolite pyrene cis-4,5-dihydrodiol was found. The results of molecular docking indicated that the regions surrounding pyrene are defined by hydrophobic amino acids which are favorable for the binding of dioxygen molecule at C4 and C5 positions of pyrene in a side-on mode. The binding positions of dioxygen are in agreement with the mass spectral analysis of the metabolite pyrene cis-4,5-dihydrodiol. In summary, this study provides a promising explanation for the possible binding behavior between pyrene and active site of NDO.

BLASTGrabber: a bioinformatic tool for visualization, analysis and sequence selection of massive BLAST data

Background

Advances in sequencing efficiency have vastly increased the sizes of biological sequence databases, including many thousands of genome-sequenced species. The BLAST algorithm remains the main search engine for retrieving sequence information, and must consequently handle data on an unprecedented scale. This has been possible due to high-performance computers and parallel processing. However, the raw BLAST output from contemporary searches involving thousands of queries becomes ill-suited for direct human processing. Few programs attempt to directly visualize and interpret BLAST output; those that do often provide a mere basic structuring of BLAST data.

Results

Here we present a bioinformatics application named BLASTGrabber suitable for high-throughput sequencing analysis. BLASTGrabber, being implemented as a Java application, is OS-independent and includes a user friendly graphical user interface. Text or XML-formatted BLAST output files can be directly imported, displayed and categorized based on BLAST statistics. Query names and FASTA headers can be analysed by text-mining. In addition to visualizing sequence alignments, BLAST data can be ordered as an interactive taxonomy tree. All modes of analysis support selection, export and storage of data. A Java interface-based plugin structure facilitates the addition of customized third party functionality.

Conclusion

The BLASTGrabber application introduces new ways of visualizing and analysing massive BLAST output data by integrating taxonomy identification, text mining capabilities and generic multi-dimensional rendering of BLAST hits. The program aims at a non-expert audience in terms of computer skills; the combination of new functionalities makes the program flexible and useful for a broad range of operations.

De novo transcriptomic analysis of hydrogen production in the green alga Chlamydomonas moewusii through RNA-Seq

BACKGROUND

Microalgae can make a significant contribution towards meeting global renewable energy needs in both carbon-based and hydrogen (H2) biofuel. The development of energy-related products from algae could be accelerated with improvements in systems biology tools, and recent advances in sequencing technology provide a platform for enhanced transcriptomic analyses. However, these techniques are still heavily reliant upon available genomic sequence data. Chlamydomonas moewusii is a unicellular green alga capable of evolving molecular H2 under both dark and light anaerobic conditions, and has high hydrogenase activity that can be rapidly induced. However, to date, there is no systematic investigation of transcriptomic profiling during induction of H2 photoproduction in this organism.

RESULTS

In this work, RNA-Seq was applied to investigate transcriptomic profiles during the dark anaerobic induction of H2 photoproduction. 156 million reads generated from 7 samples were then used for de novo assembly after data trimming. BlastX results against NCBI database and Blast2GO results were used to interpret the functions of the assembled 34,136 contigs, which were then used as the reference contigs for RNA-Seq analysis. Our results indicated that more contigs were differentially expressed during the period of early and higher H2 photoproduction, and fewer contigs were differentially expressed when H2-photoproduction rates decreased. In addition, C. moewusii and C. reinhardtii share core functional pathways, and transcripts for H2 photoproduction and anaerobic metabolite production were identified in both organisms. C. moewusii also possesses similar metabolic flexibility as C. reinhardtii, and the difference between C. moewusii and C. reinhardtii on hydrogenase expression and anaerobic fermentative pathways involved in redox balancing may explain their different profiles of hydrogenase activity and secreted anaerobic metabolites.

CONCLUSIONS

Herein, we have described a workflow using commercial software to analyze RNA-Seq data without reference genome sequence information, which can be applied to other unsequenced microorganisms. This study provided biological insights into the anaerobic fermentation and H2 photoproduction of C. moewusii, and the first transcriptomic RNA-Seq dataset of C. moewusii generated in this study also offer baseline data for further investigation (e.g. regulatory proteins related to fermentative pathway discussed in this study) of this organism as a H2-photoproduction strain.

A novel endogenous betaretrovirus group characterized from polar bears (Ursus maritimus) and giant pandas (Ailuropoda melanoleuca)

Transcriptome analysis of polar bears (Ursus maritimus) yielded sequences with highest similarity to the human endogenous retrovirus group HERV-K(HML-2). Further analysis of the polar bear draft genome identified an endogenous betaretrovirus group comprising 26 proviral copies and 231 solo LTRs. Molecular dating indicates the group originated before the divergence of bears from a common ancestor but is not present in all carnivores. Closely related sequences were identified in the giant panda (Ailuropoda melanoleuca) and characterized from its genome. We have designated the polar bear and giant panda sequences U. maritimus endogenous retrovirus (UmaERV) and A. melanoleuca endogenous retrovirus (AmeERV), respectively. Phylogenetic analysis demonstrated that the bear virus group is nested within the HERV-K supergroup among bovine and bat endogenous retroviruses suggesting a complex evolutionary history within the HERV-K group. All individual remnants of proviral sequences contain numerous frameshifts and stop codons and thus, the virus is likely non-infectious.

The PARIGA server for real time filtering and analysis of reciprocal BLAST results

BLAST-based similarity searches are commonly used in several applications involving both nucleotide and protein sequences. These applications span from simple tasks such as mapping sequences over a database to more complex procedures as clustering or annotation processes. When the amount of analysed data increases, manual inspection of BLAST results become a tedious procedure. Tools for parsing or filtering BLAST results for different purposes are then required. We describe here PARIGA (http://resources.bioinformatica.crs4.it/pariga/), a server that enables users to perform all-against-all BLAST searches on two sets of sequences selected by the user. Moreover, since it stores the two BLAST output in a python-serialized-objects database, results can be filtered according to several parameters in real-time fashion, without re-running the process and avoiding additional programming efforts. Results can be interrogated by the user using logical operations, for example to retrieve cases where two queries match same targets, or when sequences from the two datasets are reciprocal best hits, or when a query matches a target in multiple regions. The Pariga web server is designed to be a helpful tool for managing the results of sequence similarity searches. The design and implementation of the server renders all operations very fast and easy to use.

BlaSTorage: a fast package to parse, manage and store BLAST results

Background

Large-scale sequence studies requiring BLAST-based analysis produce huge amounts of data to be parsed. BLAST parsers are available, but they are often missing some important features, such as keeping all information from the raw BLAST output, allowing direct access to single results, and performing logical operations over them.

Findings

We implemented BlaSTorage, a Python package that parses multi BLAST results and returns them in a purpose-built object-database format. Unlike other BLAST parsers, BlaSTorage retains and stores all parts of BLAST results, including alignments, without loss of information; a complete API allows access to all the data components.

Conclusions

BlaSTorage shows comparable speed of more basic parser written in compiled languages as C++ and can be easily integrated into web applications or software pipelines.

Genomics, molecular imaging, bioinformatics, and bio-nano-info integration are synergistic components of translational medicine and personalized healthcare research

Supported by National Science Foundation (NSF), International Society of Intelligent Biological Medicine (ISIBM), International Journal of Computational Biology and Drug Design and International Journal of Functional Informatics and Personalized Medicine, IEEE 7th Bioinformatics and Bioengineering attracted more than 600 papers and 500 researchers and medical doctors. It was the only synergistic inter/multidisciplinary IEEE conference with 24 Keynote Lectures, 7 Tutorials, 5 Cutting-Edge Research Workshops and 32 Scientific Sessions including 11 Special Research Interest Sessions that were designed dynamically at Harvard in response to the current research trends and advances. The committee was very grateful for the IEEE Plenary Keynote Lectures given by: Dr. A. Keith Dunker (Indiana), Dr. Jun Liu (Harvard), Dr. Brian Athey (Michigan), Dr. Mark Borodovsky (Georgia Tech and President of ISIBM), Dr. Hamid Arabnia (Georgia and Vice-President of ISIBM), Dr. Ruzena Bajcsy (Berkeley and Member of United States National Academy of Engineering and Member of United States Institute of Medicine of the National Academies), Dr. Mary Yang (United States National Institutes of Health and Oak Ridge, DOE), Dr. Chih-Ming Ho (UCLA and Member of United States National Academy of Engineering and Academician of Academia Sinica), Dr. Andy Baxevanis (United States National Institutes of Health), Dr. Arif Ghafoor (Purdue), Dr. John Quackenbush (Harvard), Dr. Eric Jakobsson (UIUC), Dr. Vladimir Uversky (Indiana), Dr. Laura Elnitski (United States National Institutes of Health) and other world-class scientific leaders. The Harvard meeting was a large academic event 100% full-sponsored by IEEE financially and academically. After a rigorous peer-review process, the committee selected 27 high-quality research papers from 600 submissions. The committee is grateful for contributions from keynote speakers Dr. Russ Altman (IEEE BIBM conference keynote lecturer on combining simulation and machine learning to recognize function in 4D), Dr. Mary Qu Yang (IEEE BIBM workshop keynote lecturer on new initiatives of detecting microscopic disease using machine learning and molecular biology, http://ieeexplore.ieee.org/servlet/opac?punumber=4425386) and Dr. Jack Y. Yang (IEEE BIBM workshop keynote lecturer on data mining and knowledge discovery in translational medicine) from the first IEEE Computer Society BioInformatics and BioMedicine (IEEE BIBM) international conference and workshops, November 2-4, 2007, Silicon Valley, California, USA.