Blast Bioinformatics Tool
Blast Bioinformatics Tool - Related Videos
09:41
Blast Quantification Using Hopkinson Pressure Bars
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Cited by 2 •
2016
This protocol details the use of Hopkinson pressure bars to measure reflected blast loading from near-field explosive events. It is capable of interpolating a pressure-time history at any point on a reflective boundary and as such can be used to fully characterize the spatial and temporal variations in loading produced.
10:51
Evaluating Primary Blast Effects In Vitro
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Cited by 12 •
2017
Understanding how cells are modulated by exposure to shock waves can help identify the mechanisms behind injuries triggered from blast events. This protocol uses custom-built shock tube equipment to apply shock waves at a range of pressures to cell monolayers and to identify the subsequent effects on cell viability.
06:20
Generation of Warfighter Avatars from Weapon Training Scene Images for Blast Exposure Simulations
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Cited by 1 •
2024
A computational framework was developed to automatically reconstruct virtual scenes comprising 3D avatars of soldiers using image and/or video data. The virtual scenes help estimate blast overpressure exposure during weapon training. The tool facilitates a realistic representation of human posture in blast exposure simulations in various scenarios.
08:59
A Novel In Vitro Model of Blast Traumatic Brain Injury
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Cited by 19 •
2018
This paper describes a novel model of primary blast traumatic brain injury. A compressed-air driven shock tube is used to expose in vitro mouse hippocampal slice cultures to a single shock wave. This is a simple and rapid protocol generating a reproducible brain tissue injury with a high throughput.
13:11
Video Bioinformatics Analysis of Human Embryonic Stem Cell Colony Growth
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Cited by 17 •
2010
Video bioinformatics is the automated processing, analysis, understanding, and data mining of biological spatio-temporal data extracted from microscopic videos. The purpose of this article is to demonstrate a method for measuring human embryonic stem cell colony growth using a video bioinformatics method.
FAQs
BLAST begins with short matching words rather than attempting a full database-wide alignment immediately. It uses those initial matches to locate promising regions, then extends them into local alignments. This staged search reduces the work required for large sequence collections while preserving biologically informative matching segments, making the tool practical for molecular biology and genomics.
Substitution scores and gap penalties help rank and shape candidate alignments by accounting for sequence changes and inserted or missing positions represented as gaps. These scoring considerations influence which local regions appear most convincing. They are therefore central to interpreting similarity rather than treating every matching segment as equally informative.
The E-value adds a statistical perspective to the reported alignment. Alongside sequence similarity and alignment scores, it helps researchers judge how meaningful a database match may be. This measure is especially useful when several candidate sequences produce local matches, because it contributes an additional basis for evaluating which results deserve biological attention.
A researcher needs a biological sequence to use as the query and a sequence database for comparison. The query may be DNA, RNA, or protein, depending on the analysis. After the search, results can be examined through local alignments, substitution scores, gap penalties, and E-values to identify the most relevant candidate matches.
For a newly sequenced gene, researchers can compare its sequence with database entries to find similar genes and possible homologs. Those relationships can support preliminary functional annotation, meaning the assignment of a likely biological role based on sequence evidence. The same comparison strategy can also help assess whether an experimental cloning construct contains the expected sequence.
BLAST helps researchers examine conserved regions shared among biological sequences and identify similarities that may indicate possible evolutionary relationships. Comparisons can also contribute to organism classification when sequence patterns distinguish groups or connect them to related database entries. These uses extend the tool beyond gene annotation into comparative biology and broader studies of biological diversity.