Blast Database
Blast Database - Related Videos
16:17
The ITS2 Database
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Cited by 52 •
2012
The ITS2 Database is a workbench for phylogenetic inference simultaneously considering sequence and secondary structure of the internal transcribed spacer 2. This includes data collection with accurate annotation, structure prediction, multiple sequence-structure alignment and fast tree calculation. In a nutshell, this workbench simplifies first phylogenetic analyses to a few clicks.
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.
01:28
Database Searching
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2026
Effective research begins with the ability to navigate academic databases efficiently. Unlike general web search engines, scholarly databases are designed to index peer-reviewed articles, conference proceedings, and other credible academic publications. Developing a structured search strategy ensures that researchers can identify relevant, high-quality sources with precision. For example, when studying how sleep patterns relate to circadian rhythms in humans, researchers should move beyond...
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.
11:18
Generation of Comprehensive Thoracic Oncology Database - Tool for Translational Research
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Cited by 5 •
2011
A thoracic oncology database was developed to serve as a comprehensive repository for clinical and laboratory data for the purposes of translational research. The database will serve translational cancer researchers within the Thoracic Oncology Research Program. This database is adaptable to other cancer models, as well as other human diseases.
FAQs
BLAST searches begin by locating short matching words between the query and database records. The algorithm then extends promising word hits into local alignments rather than comparing every sequence position equally. This staged process concentrates computation on regions with initial evidence of similarity, producing candidate alignments that can be scored and ranked for biological interpretation.
Alignment scores summarize how strongly a query and database record agree within an aligned region, whereas the E-value estimates how likely a match of that quality could arise by chance. Considering both measures helps distinguish stronger, less random-looking results from weaker matches. These values help researchers evaluate possible sequence relationships and prioritize records for further interpretation.
Choosing a nucleotide or protein database should match the type of biological question and query being analyzed. Nucleotide records are appropriate when the comparison concerns DNA or RNA sequences, while protein records support comparisons involving protein sequences. This choice affects which known records can be matched and therefore shapes the evidence available for annotation or relationship analysis.
A reproducible search requires more than submitting a sequence: researchers should select the intended database, retain the search conditions, and note when the analysis was performed. Database content and annotations change over time, so the same query may later be evaluated against different records. Recording these details preserves the context needed to interpret and repeat the analysis.
Sequence comparisons can support gene annotation by connecting an uncharacterized sequence with known records, and they can help identify homologous proteins. The resulting matches also provide evidence for investigating evolutionary relationships. In each case, the search supplies candidate sequence relationships rather than a standalone biological conclusion, so the relevance of matched records must guide interpretation.
When characterizing a newly sequenced organism, researchers can compare its DNA, RNA, or protein sequences against known records to identify possible relationships. The database should reflect whether the query is nucleotide or protein based, while scores and E-values help rank the resulting matches. This combination supports a systematic route from raw sequence to biological interpretation.