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Most cited protocols related to «BP 400»

1
Efficient Read Mapping with Distinctive Alignments
Cited 1465 times
Like BLAST, both BLAT and SSAHA2 report all significant alignments or typically tens of top-scoring alignments, but this is not the most desired output in read mapping. We are typically more interested in the best alignment or best few alignments, covering each region of the query sequence. For example, suppose a 1000 bp query sequence consists of a 900 bp segment from one chromosome and a 100 bp segment from another chromosome; 400 bp out of the 900 bp segment is a highly repetitive sequence. For BLAST, to know this is a chimeric read we would need to ask it to report all the alignments of the 400 bp repeat, which is costly and wasteful because in general we are not interested in alignments of short repetitive sequences contained in a longer unique sequence. On this example, a useful output would be to report one alignment each for the 900 bp and the 100 bp segment, and to indicate if the two segments have good suboptimal alignments that may render the best alignment unreliable. Such output simplifies downstream analyses and saves time on reconstructing the detailed alignments of the repetitive sequence.
In BWA-SW, we say two alignments are distinct if the length of the overlapping region on the query is less than half of the length of the shorter query segment. We aim to find a set of distinct alignments which maximizes the sum of scores of each alignment in the set. This problem can be solved by dynamic programming, but as in our case a read is usually aligned entirely, a greedy approximation would work well. In the practical implementation, we sort the local alignments based on their alignment scores, scan the sorted list from the best one and keep an alignment if it is distinct from all the kept alignments with larger scores; if alignment a2 is rejected because it is not distinctive from a1, we regard a2 to be a suboptimal alignment to a1 and use this information to approximate the mapping quality (Section 2.7).
Because we only retain alignments largely non-overlapping on the query sequence, we might as well discard seeds that do not contribute to the final alignments. Detecting such seeds can be done with another heuristic before the Smith–Waterman extension and time spent on unnecessary extension can thus be saved. To identify these seeds, we chain seeds that are contained in a band (default band width 50 bp). If on the query sequence a short chain is fully contained in a long chain and the number of seeds in the short chain is below one-tenth of the number of seeds in the long chain, we discard all the seeds in the short chain, based on the observation that the short chain can rarely lead to a better alignment than the long chain in this case. Unlike the Z-best strategy, this heuristic does not have a noticeable effect on alignment accuracy. On 1000 10 kb simulated data, it halves the running time with no reduction in accuracy.
Li H, & Durbin R. (2010). Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics, 26(5), 589-595.
Publication 2010
BP 100 BP 400 Chimera Chromosomes Plant Embryos Radionuclide Imaging Repetitive Region Sequence Alignment Toxic Epidermal Necrolysis
2
Inferring Demographic Parameters from DNA Sequences
Cited 176 times
We have tested our program ability to recover demographic parameters from DNA sequence data in four relatively plausible but distinct scenarios of population differentiation involving one to ten populations with migration (see Figure 1). In all cases, we simulated with fastsimcoal2 400,000 unlinked regions of 50 bp, thus totaling 20 Mb of DNA sequences, assuming a mutation rate of 2.5×10−8 bp−1 per generation and an infinite-site model. Pseudo-observed SFS were also directly computed with fastsimcoal2. Parameters were estimated independently from ten data sets generated under each model. For each data set generated under models with one to three populations, we performed 50 parameter estimations via ECM maximization, and each time retained the parameter set with maximum likelihood. For the model with 10 populations we only performed 20 estimations per data sets, and used 50,000 simulations instead of 100,000 for the other models to estimate the expected SFS due to long computation times. We describe the four tested models in Figure 1, and the used parameter values are showed as red dots in Figures 2, 3, S4 and S5. Absolute numbers (generations, population sizes) were obtained by assuming that the mutation rate of 2.5×10−8 bp−1 per generation was known.
As a benchmark, we used to infer the demographic parameters in scenarios shown in Figure 1A–1C involving up to three populations. For each generated data set, we performed 50 parameter estimations using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization method implemented in , and we retained the parameters associated with the maximum likelihood. We followed 's manual specification to set reasonable upper and lower bounds of the search ranges of the parameter. In all cases, the expected SFS was estimated by extrapolating the SFS inferred from 3 grid sizes set to 40, 50 and 60, which are in all cases larger than our maximum samples sizes (30 in the IM model case). The composite likelihood was computed using 's multinomial model, which is in fact a product of Poisson likelihoods, where the expected model entries are scaled to sum up to 1. This likelihood also ignores information about the expected and observed numbers of monomorphic and polymorphic sites used in our likelihood formulation (as well as in [20] (link)). Therefore, the ratio should be equal to showing that barring the terms, the two CLs differ by a single constant value. The difference between likelihoods computed with fastsimcoal and is illustrated in Figure S3 for the case of the bottleneck scenarios shown in Figures 1A. It shows that when monomorphic sites are not taken into account, fastsimcoal and indeed produce essentially identical likelihood profiles around true parameters. However, when monomorphic sites are used in the likelihood, the shape of the likelihood profiles differs, making it more or less peaky depending on the parameter. There is thus no clear advantage in using one or the other likelihood form for this scenario, but our use of monomorphic sites allows us to directly get absolute values of the parameters. We report in Figures 2, S4 and S5 only the results obtained for data sets for which 's best log likelihood was less than 10% lower than the largest log-likelihood obtained with the other data sets, and we considered not to have converged for the discarded data sets.
Excoffier L., Dupanloup I., Huerta-Sánchez E., Sousa V.C, & Foll M. (2013). Robust Demographic Inference from Genomic and SNP Data. PLoS Genetics, 9(10), e1003905.
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Publication 2013
ADRB2 protein, human arabinose 1,5-diphosphate BP 400 DNA Sequence Mutation Neutrophil Population Group
3
Phylogenetic Analysis of Bacterial Genomes
Cited 60 times
Phylogenetic trees were generated by exporting ribosomal protein gene sequences from the strain database as an XMFA file containing each locus as an aligned block. ClonalFrame analysis was performed for single genus datasets using ClonalFrame version 1.2 (Didelot & Falush, 2007 (link)) with default parameters. For larger datasets, up to the entire bacterial domain, the XMFA file was converted to an aligned concatenated sequence for Neighbor-joining tree analysis using Mega version 5 (Kumar et al., 2008 (link)) with ambiguous positions removed for each sequence pair. Split decomposition analysis was performed using SplitsTree version 4 (Huson & Bryant, 2006 (link)) for species level datasets. Dendroscope (Huson et al., 2007 (link)) was used to visualize large trees.
To assess congruence, maximum-likelihood (ML) phylogenetic trees were constructed using Paup version 4 beta 10 (Swofford, 1998 ) on finished genomes from the entire Bacilli class (n=144). ML trees for ten ribosomal protein genes (rpsB, rpsC, rpsD, rpsE, rpsG, rpsI, rpsK, rpsL, rpsP and rpsT) with sizes between 400 – 1100 bp were computed and compared using the Shimodaira-Hasegawa test, which determines if significant differences occur among the tree topologies (differences in log likelihood, Δ-ln L). Randomisation tests were then performed (Holmes et al., 1999 (link)), where the Δ –ln L values for each of the genes were compared to the equivalent values computed for 200 random trees created from each gene. This analysis was carried out on finished genomes from the entire Bacilli class (n=144).
Jolley K.A., Bliss C.M., Bennett J.S., Bratcher H.B., Brehony C., Colles F.M., Wimalarathna H., Harrison O.B., Sheppard S.K., Cody A.J, & Maiden M.C. (2012). Ribosomal multilocus sequence typing: universal characterization of bacteria from domain to strain. Microbiology, 158(Pt 4), 1005-1015.
Publication 2012
Bacteria BP 400 Gene Products, Protein Genes Genome Lacticaseibacillus casei Ribosomal Proteins Ribosomes Strains Trees
4
Precise Sequence Error Correction for Pyrosequencing
Cited 43 times
The advantage of splitting the pyrosequencing and PCR noise removal steps is that it allows a more appropriate model to be used for the removal of the PCR single base errors. We used the same ideas as above to develop a procedure for removing PCR errors. We define a distance that reflects the probability that a given read r¯ could have been generated from a true sequence S¯ , given PCR error. This probability is simply the sum of the necessary nucleotide transitions i.e. the probability that a nucleotide m is observed when the true nucleotide is n, P (m|n). The total probability of the read will be the product of these and we take the negative logarithm to generate a sequence error corrected distance between zero and infinity. We also normalise by the alignment length A:
e(r¯,S¯)=−log[P(r¯ | S¯)]=−log[∏l=1MP(rl=m | sl=n)]/A=∑l=1A−log[P(rl=m | sl=n)]/A
This requires alignment of the read to the sequence. Alignment was performed with a specially modified version of the Needleman-Wunsch algorithm with a reduced gap cost for homopolymer insertion and deletions. This accounted for the possibility of pyrosequencing noise on low frequency reads which may not have been removed in the flowgram clustering. Gap penalties were included in the distance measure.
The nucleotide transition probabilities were calculated by comparing all reads with pyrosequencing noise removed from the three 'Even' V2 data sets with the known control sequences. These are shown in Table 2. It is interesting to note the higher frequencies of transitions (A ↔ G and C ↔ T ) versus transversions. This was also found for the V5 and Titanium data. Indeed the relative magnitudes of these probabilities were similar for all the data sets, perhaps because standard Taq polymerases were used throughout, and the per cycle error rates were the same order of magnitude as has been observed by other methods for Taq polymerases [17 (link)]. We therefore used the transition probabilities in Table 2 for calculating sequence distances in all the data sets.
We used a mixture model to cluster the sequences, just as for pyrosequencing noise removal, where each component of the mixture corresponds to a true sequence about which observed noisy reads are distributed. The relative weights of each component are the true relative frequencies of the sequences. The reads are assumed to be distributed as exponentially decaying functions of their sequence error corrected distance from these true sequences. The magnitude of the sequence noise is described by the characteristic length of these exponentials, σs . A maximum likelihood fit of the mixture model can be obtained using an Expectation-Maximization algorithm initialised using the clusters formed from a hierarchical clustering of sequences at a given distance cut-off, cs . In this study we used parameter values of σs = 0.033 and cs = 0.08, parameters that experience has taught us work well for GS FLX data. For the Titanium data we compared two different values for σs, 0.1 and 0.04, whilst keeping cs = 0.08. A standard gap was given a penalty of 15.0 and a homopolymer gap, 4.0. Prior to our sequence clustering step we truncated at 220 bp for GS FLX and 400 bp for Titanium because of the increase in error rates at the ends of reads.
Quince C., Lanzen A., Davenport R.J, & Turnbaugh P.J. (2011). Removing Noise From Pyrosequenced Amplicons. BMC Bioinformatics, 12, 38.
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Publication 2011
BP 400 Gene Deletion Nucleotides Taq Polymerase Teaching Titanium
5
Tn5 Transposase Activation and DNA Fragmentation
Cited 42 times
Tn5 reactions were assembled by mixing 14 μL H2O, 4 μL 5× TAPS-MgCl2-PEG 8000 or 5× TAPS-DMF, 1 μL target DNA at 50 ng/μL, 1 μL of the Tn5, preassembled with A/B-MEDS oligonucleotides at 12.5 mM. Buffers used were as follows: 5× TAPS-PEG 8000 (50 mM TAPS-NaOH at pH 8.5 [RT], 25 mM MgCl2, 40% PEG 8000) or 5× TAPS-DMF (50 mM TAPS-NaOH at pH 8.5 [RT], 25 mM MgCl2, 50% DMF). For library production, the reactions were incubated for 7 min at 55°C, and then 5 μL 0.2% SDS (final 0.02%) was added and Tn5 was inactivated for 7 min at room temperature or 55°C. Typically 5 μL of the Tn5 reactions was used directly in the subsequent enrichment PCR amplification of libraries for the Illumina sequencers. Successful libraries were also generated using only the index adaptors as they contain the entire sequence of the PCR primers (FC-121-1012 and FC-121-1011).
For testing transposase activity, reactions were set up as above, with 50 ng HMW DNA as substrate. To stop the reactions, 0.5 μL proteinase K (20 mg/mL; Qiagen) was added to each reaction, followed by incubation for 7 min at 55°C. The samples were then analyzed by agarose gel electrophoresis. Typically all of the HMW DNA was converted to fragments of average size 400–500 bp. An image of a typical activity assay is shown in Figure 1C.
Picelli S., Björklund Å.K., Reinius B., Sagasser S., Winberg G, & Sandberg R. (2014). Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Research, 24(12), 2033-2040.
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Publication 2014
Biological Assay BP 400 Buffers DNA Library Electrophoresis, Agar Gel Endopeptidase K Magnesium Chloride Oligonucleotide Primers Oligonucleotides polyethylene glycol 8000 Transposase

Most recents protocols related to «BP 400»

1
Genome Sequencing of the Asian Vine Snake
To generate a reference genome sequence of the Asian vine snake, muscle tissue from a male green snake (ID: CIB119038) from Xishuangbanna, Yunnan Province, China, was collected. High molecular weight genomic DNA was prepared using the CTAB method, followed by purification using a QIAGEN® Genomic kit (QIAGEN, Valencia, CA, USA) for sequencing according to the standard procedures provided by the manufacturer.
For genome sequencing, DNA was extracted using the SDS method. DNA degradation and extracted DNA contamination were monitored using 1% agarose gels. DNA purity was then detected using a NanoDrop™ One UV-Vis Spectrophotometer (Thermo Fisher Scientific, USA), with OD 260/280 ranging from 1.8 to 2.0 and OD 260/230 ranging from 2.0 to 2.2. Lastly, the DNA concentration was further measured using a Qubit® 4.0 Fluorometer (Invitrogen, USA). In total, 3–4 μg of DNA per sample was used as input material for the ONT library preparations. After the sample was qualified, size selection of long DNA fragments was performed using the PippinHT system (Sage Science, USA). The DNA fragments ends were then repaired, and A-ligation reaction was conducted using a NEBNext Ultra II End Repair/dA-tailing Kit (Cat# E7546). The adapter in SQK-LSK109 (Oxford Nanopore Technologies, UK) was used for further ligation reactions and the DNA library was measured using a Qubit® 4.0 Fluorometer (Invitrogen, USA). A DNA library (700 ng) was constructed and long-read sequencing was performed on a Nanopore PromethION sequencer (Oxford Nanopore Technologies, UK).
For short-read sequencing, a paired-end library was conducted with an insert size of 300 bp and 100 bp paired-end reads, then sequenced using the MGISEQ-2000 platform following the manufacturer’s standard protocols.
For Hi-C sequencing, muscle cells from the Asian vine snake were fixed with formaldehyde, followed by restriction enzyme digestion. Nuclei were extracted by lysing the cross-linked tissue. The cohesive ends were filled in by adding biotinylated nucleotides, and the free blunt ends were ligated. The cross-linking was reversed, and DNA was purified to remove proteins. The purified DNA was then sheared to a length of ∼400 bp and point ligation junctions were pulled down. The Hi-C libraries were sequenced using the Illumina HiSeq platform with PE150 short reads.
Tang C.Y., Zhang X., Xu X., Sun S., Peng C., Song M.H., Yan C., Sun H., Liu M., Xie L., Luo S.J, & Li J.T. (2023). Genetic mapping and molecular mechanism behind color variation in the Asian vine snake. Genome Biology, 24, 46.
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Publication 2023
Asian Persons BP 400 Cell Nucleus Cetrimonium Bromide Digestion DNA, A-Form DNA Contamination DNA Library DNA Restriction Enzymes Formaldehyde Gels Genome Ligation Males Muscle Cells Muscle Tissue Nucleotides Proteins Sepharose Snakes Tissues
2
Comprehensive genome sequencing and T3E analysis
The genome sequencing was performed by combined technologies including PacBio RS II and Illumina HiSeq2000. The single molecule real-time (SMRT) sequencing was performed on the PacBio RS II platform with a 20 kb library. A paired-end library with an insert size of 400 bp was sequenced using an Illumina HiSeq2000 using PE150 strategy.
The platform and bioinformatics methods were listed in Table S2 (see supporting information). Since “Ralsto T3E” was the website specially developed for R. solanacearum strains, it was chosen for the main prediction. Three other prediction software platforms MAUVE, BRIG-0.95 (BLAST options is -evalue 1e-5), FastANI (fastANI -q genome1.fa -r genome2.fa -o output.txt -FragLen), and the representative genome GMI1000 were utilized to predict and rectify the prediction result of the T3Es in HA4-1 and HZAU091. Each predicted T3E gene of HZAU091 was then amplified by polymerase chain reaction and confirmed or corrected by sanger sequencing.
Huang M., Tan X., Song B., Wang Y., Cheng D., Wang B, & Chen H. (2023). Comparative genomic analysis of Ralstonia solanacearum reveals candidate avirulence effectors in HA4-1 triggering wild potato immunity. Frontiers in Plant Science, 14, 1075042.
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Publication 2023
BP 400 DNA Library Genes Genome Polymerase Chain Reaction Strains
3
Molecular Identification of Tick Species in Malaysian Borneo
Ticks were collected in Sarawak, Malaysian Borneo, from protected primary forests, Gunung Gading National Park (1.69° N 109.85 °E) and Kubah National Park (1.61° N 110.20° E) in November 2018; and from an oil palm plantation (3.36° N 113.69° E) in March 2019. Questing ticks were collected by dragging white flannel cloths over the forest floor, and feeding ticks were removed from rodents that were trapped during the sampling period. All ticks were kept separately in 70 % ethanol and stored at −20 °C until sample processing and DNA extraction.
Ticks were morphologically identified to their genera or species levels based on taxonomic keys [33–37 (link)]. Molecular identification was performed using the previously published primers [38 (link)] (Table S1, available in the online version of this article) for the amplification of an approximately 400 bp fragment of the mitochondrial 16S rDNA partial sequence using one leg from the ticks by using the hot alkaline extraction method previously described by Mtambo et al. [39 (link)] with some modifications. Briefly, the tick leg was incubated at 95 °C for 10 min after adding 10 µl of 100 nM of sodium hydroxide, followed by addition of 2 µl Tris-hydrochloride buffer (pH 7.0). A total of 209 feeding and questing ticks of different developmental stages and statuses from I. granulatus (n=32), H. hystricis (n=36), Haemaphysalis shimoga (n=109), Dermacentor compactus (n=4), D. steini (n=24) and Dermacentor atrosignatus (n=4) were used for this study. Details of the tick samples and collection sites are provided in Table 1.
Lau A.C., Mohamed W.M., Nakao R., Onuma M., Qiu Y., Nakajima N., Shimozuru M., Mohd-Azlan J., Moustafa M.A, & Tsubota T. (2023). The dynamics of the microbiome in Ixodidae are shaped by tick ontogeny and pathogens in Sarawak, Malaysian Borneo. Microbial Genomics, 9(2), mgen000954.
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Publication 2023
BP 400 Dermacentor DNA, Ribosomal Ethanol Forests Mitochondria Oligonucleotide Primers Palm Oil Rodent Sodium Hydroxide Ticks Tromethamine
4
Chromatin Immunoprecipitation of H3K36me2
The steps were followed as described for the previous ChIP assay [26 (link)]. The cultured cells were first fixed with formaldehyde. Then, cells were sonicated and fragments of 400–600 bp in size were obtained to cross-link chromatin. Samples were diluted and pretreated with blocked A-Sepharose beads for 30 min. Supernatants were immunoprecipitated overnight with anti-H3K36me2 or anti-IgG antibodies. Finally, the beads were uncross-linked from the protein and the occupancy of H3K36me2 on the MAZ CpG promoter was detected by RT-qPCR.
Zhu M., Zhang R.N., Zhang H., Qu C.B., Zhang X.C., Ren L.X., Yang Z, & Gu J.F. (2023). PCGF6/MAX/KDM5D facilitates MAZ/CDK4 axis expression and pRCC progression by hypomethylation of the DNA promoter. Epigenetics & Chromatin, 16, 9.
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Publication 2023
Anti-Antibodies BP 400 Cells Chromatin Cultured Cells Formaldehyde Immunoprecipitation, Chromatin link protein Sepharose
5
Metagenomic Analysis of Rumen Microbiome
DNA was extracted from the rumen contents using Soil DNA Kit (MOBIO, Carlsbad, CA, USA). The concentration and purity of the extracted DNA were determined with TBS-380 fluorometer (Turner Biosystems, Sunnyvale, CA, USA) and NanoDrop 2000 (NanoDrop Technologies, Wilmington, DE, USA), respectively. The DNA was fragmented to fragments of approximately 400 bp using Covaris M220 (Gene Company Limited, Hongkong, China) for library construction. Sequencing was performed using the Illumina NovaSeq6000 (Illumina Inc, San Diego, CA, USA) sequencing platform. Sequencing adapters were removed with FAST software (version 0.20.0), and low-quality reads (length < 50 bp in length or quality value < 20 or with N bases) were removed [26 (link)]. Reads were aligned to the Bos Taurus reference genome assembly using BWA 0.7.9a (http://bio-bwa.sourceforge.net). Metagenomic sequencing data were assembled with MULTIPLE MEGAHIT (Version 1.1.2) [27 (link)]. Overlapping sequences with lengths ≥ 300 bp were selected as the final assembly results and used for further gene annotation. Metagenes were used to predict the best candidate open reading frames (ORFs) [28 (link)]. The predicted ORFs with length ≥ 100 bp were retrieved. A cluster analysis of a non-redundant gene catalog with sequence homology and 90% coverage was constructed using CD-HIT (version 4.6.1) [29 (link)]. Subsequently, representative sequences of the non-redundant gene catalog were aligned with the NCBI NR database using BLASTP (version 2.2.28 +) (the e-value cutoff for the best match was 1e −5) to obtain annotation results and species abundance degree [30 (link)]. Finally, a BLAST search (version 2.2.28 +) with an optimization criterion cutoff of 1e −5, annotated against the KEGG database [31 (link)], was performed using USEARCH (http://www.drive5.com/usearch/) CAZY annotation [32 ].
Shi J., Lei Y., Wu J., Li Z., Zhang X., Jia L., Wang Y., Ma Y., Zhang K., Cheng Q., Zhang Z., Ma Y, & Lei Z. (2023). Antimicrobial peptides act on the rumen microbiome and metabolome affecting the performance of castrated bulls. Journal of Animal Science and Biotechnology, 14, 31.
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Publication 2023
Bos taurus BP 100 BP 400 DNA Library Gene Annotation Genes Genes, Reiterated Genes, vif Genome Metagenome Open Reading Frames Rumen

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More about "BP 400"

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