BlastGUI: A Python‐based Cross‐platform Local BLAST Visualization Software

Du, Zongjun; Wu, Qing; Wang, Tianzhu; Chen, Defang; Huang, Xiaoli; Yang, Wei; Luo, Wei

doi:10.1002/minf.201900120

Cited by 8 publications

(11 citation statements)

References 5 publications

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“…A drawback is that only Traes accessions could be mapped and not UniProt ones. Using BlastGUI software [ 62 ] to blast the 8738 (95%) UniProtKB accessions, we retrieved 8288 Traes identifiers, of which 1949 (24%) could be mapped in T. aestivum Pathway tools cellular overview ( Supplementary Figure S7A ).…”

Section: Resultsmentioning

confidence: 99%

“…The 8738 UniProt FASTA sequences were blasted against the 133,346 Traes accessions [2] hosted by the EnsemblPlants T. aestivum (http://ftp.ebi.ac.uk/ensemblgenomes/ pub/release-51/plants/fasta/triticum_aestivum/pep/ (accessed on 26 February 2020)) using BlastGUI [62] and an e-value < 1 × 10 −6 . The other BlastGUI parameters were: threads = 4, outfmts = 6, other cmd = _max_target_seqs_1.…”

Section: Data Miningmentioning

confidence: 99%

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Mining the Wheat Grain Proteome

Vincent

Bui

Ram

et al. 2022

IJMS

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Bread wheat is the most widely cultivated crop worldwide, used in the production of food products and a feed source for animals. Selection tools that can be applied early in the breeding cycle are needed to accelerate genetic gain for increased wheat production while maintaining or improving grain quality if demand from human population growth is to be fulfilled. Proteomics screening assays of wheat flour can assist breeders to select the best performing breeding lines and discard the worst lines. In this study, we optimised a robust LC–MS shotgun quantitative proteomics method to screen thousands of wheat genotypes. Using 6 cultivars and 4 replicates, we tested 3 resuspension ratios (50, 25, and 17 µL/mg), 2 extraction buffers (with urea or guanidine-hydrochloride), 3 sets of proteases (chymotrypsin, Glu-C, and trypsin/Lys-C), and multiple LC settings. Protein identifications by LC–MS/MS were used to select the best parameters. A total 8738 wheat proteins were identified. The best method was validated on an independent set of 96 cultivars and peptides quantities were normalised using sample weights, an internal standard, and quality controls. Data mining tools found particularly useful to explore the flour proteome are presented (UniProt Retrieve/ID mapping tool, KEGG, AgriGO, REVIGO, and Pathway Tools).

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Section: Resultsmentioning

confidence: 99%

Section: Data Miningmentioning

confidence: 99%

Mining the Wheat Grain Proteome

Vincent

Bui

Ram

et al. 2022

IJMS

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“…After every option is defined, click the "BLAST" button. (5) Open File: This "Open File" button appears when the analysis is run normally. Click the button to view on Notepad or WordPad.…”

Section: Blast Analysismentioning

confidence: 99%

NBLAST: a graphical user interface-based two-way BLAST software with a dot plot viewer

Choi¹,

Choi²,

Kim³

et al. 2022

Genomics Inform

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BLAST, a basic bioinformatics tool for searching local sequence similarity, has been one of the most widely used bioinformatics programs since its introduction in 1990. Users generally use the web-based NCBI-BLAST program for BLAST analysis. However, users with large sequence data are often faced with a problem of upload size limitation while using the web-based BLAST program. This proves inconvenient as scientists often want to run BLAST on their own data, such as transcriptome or whole genome sequences. To overcome this issue, we developed NBLAST, a graphical user interface-based BLAST program that employs a two-way system, allowing the use of input sequences either as “query” or “target” in the BLAST analysis. NBLAST is also equipped with a dot plot viewer, thus allowing researchers to create custom database for BLAST and run a dot plot similarity analysis within a single program. It is available to access to the NBLAST with http://nbitglobal.com/nblast.

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“…Moreover, NCBI provides an independent program BLAST + for users that dramatically accelerates the speed of long sequences query and chromosome length databases query to address the problem of slow-speed BLAST online comparison [78]. Recently, Du et al designed a cross-platform local BLAST visualization software developed in Python using the in-built graphical user interface (GUI) module TKinter [79]. BlastGUI, as it is known, utilizes BLAST + as a comparison tool to perform the local operation and sequence comparison visualization.…”

Section: Comparative Genomicsmentioning

confidence: 99%

“…BlastGUI, as it is known, utilizes BLAST + as a comparison tool to perform the local operation and sequence comparison visualization. This user-friendly tool allows users without familiarity in computational coding and basic computer skills to compare a sequence directly without additional formatting efforts [79]. BlastGUI preprocesses the input sequence, so the computational complexity of sequence comparison is low.…”

Section: Comparative Genomicsmentioning

confidence: 99%

In silico Methods for Identification of Potential Therapeutic Targets

Zhang

Yang

et al. 2021

Interdiscip Sci Comput Life Sci

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At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery. Graphical abstract

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BlastGUI: A Python‐based Cross‐platform Local BLAST Visualization Software

Cited by 8 publications

References 5 publications

Mining the Wheat Grain Proteome

Mining the Wheat Grain Proteome

NBLAST: a graphical user interface-based two-way BLAST software with a dot plot viewer

In silico Methods for Identification of Potential Therapeutic Targets

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