Genomic Library
They are commonly used in genetic research to study gene structure, expression, and function.
These libraries are typically created by extracting and fragmenting genomic DNA, then inserting the fragments into vectors for storage and replication in host cells.
Researchers can then screen the libraries to identify specific genes or genomic regions of interest.
Accurate and reproducible methods for constructing and utilizing genomic libraries are essential for advancing our understaning of genomes and facilitating advanaces in areas like personalized medicine and evolutionary biology.
PubCompare.ai's AI-powered tools can help optimze your genomic library reserch by identifying the best protocols from published literature, pre-prints, and patents, ensuring the quality and reliability of your work.
Most cited protocols related to «Genomic Library»
Once the library is chosen, we use the Jellyfish multithreaded k-mer counter [19 (link)] to create a database containing every distinct 31-mer in the library. Once the database is complete, the 4-byte spaces Jellyfish used to store the k-mer counts in the database file are instead used by Kraken to store the taxonomic ID numbers of the k-mers’ LCA values. After the database has been created by Jellyfish, the genomic sequences in the library are processed one at a time. For each sequence, the taxon associated with it is used to set the stored LCA values of all k-mers in the sequence. As sequences are processed, if a k-mer from a sequence has had its LCA value previously set, then the LCA of the stored value and the current sequence’s taxon is calculated and that LCA is stored for the k-mer. Taxon information is obtained from the NCBI taxonomy database.
Standard library prep was performed according to the manufacturer's instructions described in the Chromium Genome User Guide Rev A (
Most recents protocols related to «Genomic Library»
Example 3
We generated and analyzed a collection of 14 early-passage (passage ≤9) human pES cell lines for the persistence of haploid cells. All cell lines originated from activated oocytes displaying second polar body extrusion and a single pronucleus. We initially utilized chromosome counting by metaphase spreading and G-banding as a method for unambiguous and quantitative discovery of rare haploid nuclei. Among ten individual pES cell lines, a low proportion of haploid metaphases was found exclusively in a single cell line, pES10 (1.3%, Table 1B). We also used viable FACS with Hoechst 33342 staining, aiming to isolate cells with a DNA content corresponding to less than two chromosomal copies (2c) from four additional lines, leading to the successful enrichment of haploid cells from a second cell line, pES12 (Table 2).
Two individual haploid-enriched ES cell lines were established from both pES10 and pES12 (hereafter referred to as h-pES10 and h-pES12) within five to six rounds of 1c-cell FACS enrichment and expansion (
Both h-pES10 and h-pES12 exhibited classical human pluripotent stem cell features, including typical colony morphology and alkaline phosphatase activity (
Haploid cells are valuable for loss-of-function genetic screening because phenotypically-selectable mutants can be identified upon disruption of a single allele. To demonstrate the applicability of this principle in haploid human ES cells, we generated a genome-wide mutant library using a piggyBac transposon gene trap system that targets transcriptionally active loci (
Example 3
1 μL of genomic DNA was processed using a NEBNext dsDNA Fragmentase kit (New England Biolabs) by following the manufacturer's protocol. Incubation time was extended to 45 minutes at 37° C. The fragmentation reaction was stopped by adding 5 μL of 0.5M EDTA pH 8.0, and was purified by adding 2× volumes of Ampure XP beads (Beckman Coulter, A63881) according to the manufacturer's protocol. Fragmented DNA was analyzed on a Bioanalyzer with a High Sensitivity DNA kit (Agilent). The size range of fragmented DNA was typically from about 100 bp to about 200 bp with a peak of about 150 bp.
Average Nucleotide Identity (ANI) [17 ] measures nucleotide-level genome similarity between the coding regions of two genomes. The complete genome sequence was submitted in FASTA format as an input file. This tool gives the similarity index percentage [18 (link)]. ANI is computed using the formula [19 ]:
Genome annotation of Lelliottia amnigena was done by RAST (Rapid Annotation using Subsystems Technology), PATRIC (The Pathosystems Resource Integration Center), and PGAP (Prokaryotic Genome Annotation Pipeline). Assembled genome sequence was submitted in RAST in FASTA format as input files, assigned functions to the genes. It also predicted the subsystems which were represented in the genome. By using this information, it reconstructs the metabolic network and makes the output file easily downloadable. Similarly, contigs were submitted in PATRIC as input files which provided annotation, subsystem summary, phylogenetic tree, and pathways. NCBI PGAP was used to annotate the bacterial genome where the complete genomic sequence was submitted in FASTA format as an input file, and it predicted the protein-coding regions and functional genome units like tRNAs, rRNA, pseudogenes, transposons, and mobile elements.
Expression levels of contigs in each sample were measured with Salmon (Patro et al., 2017 (link)), and contigs with no raw counts were removed. To annotate the remaining contigs containing autonomous TEs, BLASTp and BLASTx were used against Repbase with an E-value cutoff of 1E-5 and 1E-10, respectively. The aligned length coverage was set to exceed 80% of the queried transcriptome contigs. To annotate contigs containing non-autonomous TEs, RepeatMasker was used with our Ranodon-derived genomic repeat library of non-autonomous TEs (LARD-, TRIM-, MITE-, and SINE-annotated contigs) and the requirement that the transcriptome/genomic contig overlap was >80 bp long, >80% identical in sequence, and covered >80% of the length of the genomic contig. Contigs annotated as conflicting autonomous and non-autonomous TEs were filtered out.
To identify contigs that contained endogenous R. sibiricus genes, the Trinotate annotation suite (Bryant et al., 2017 (link)) was used with an E-value cutoff of 1E-5 for both BLASTx and BLASTp against the Uniport database, and 1E-5 for HMMER against the Pfam database (Wheeler and Eddy, 2013 (link)). To identify contigs that contained both a TE and an endogenous gene (i.e., putative cases where a TE and a gene were co-transcribed on a single transcript), all contigs that were annotated both by Repbase and Trinotate were examined, and the ones annotated by Trinotate to contain a TE-encoded protein (i.e., the contigs where Repbase and Trinotate annotations were in agreement) were not further considered. The remaining contigs annotated by Trinotate to contain a non-TE gene (i.e., an endogenous Ranodon gene) and also annotated either by Repbase to include a TE-encoded protein or by blastn to include a non-autonomous TE were filtered out for the expression analysis.
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More about "Genomic Library"
These libraries are widely used in genetic research to study gene structure, expression, and function, as well as to facilitate advancements in personalized medicine and evolutionary biology.
The construction of genomic libraries typically involves extracting and fragmenting genomic DNA, followed by the insertion of these fragments into specialized vectors for storage and replication in host cells.
Researchers can then screen the libraries to identify specific genes or genomic regions of interest, using a variety of techniques such as hybridization, PCR, or next-generation sequencing platforms like the HiSeq 2500, HiSeq 2000, MiSeq, NovaSeq 6000, and HiSeq 4000.
Accurate and reproducible methods for constructing and utilizing genomic libraries are essential for advancing our understanding of genomes.
This includes the use of high-quality DNA extraction kits, such as the DNeasy Blood and Tissue Kit, and quality control measures, such as the Agilent 2100 Bioanalyzer, to ensure the integrity and purity of the DNA samples.
PubCompare.ai's AI-powered tools can help optimize your genomic library research by identifying the best protocols from published literature, preprints, and patents, ensuring the quality and reliability of your work.
By leveraging these data-driven insights, you can elevate your research and contribute to the growing field of genomics and personalized medicine.
Typo: The contruction of genomic libraries typically involves extracting and fragmenting genomic DNA, followed by the insertion of these fragments into specialized vectors for storage and replication in host cells.