AMXdata
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA792909 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR17461308_1.fastq.gz | Fastqsanger.gz | |||
| SRR17461308_2.fastq.gz | Fastqsanger.gz | |||
| SRR17461309_1.fastq.gz | Fastqsanger.gz | |||
| SRR17461309_2.fastq.gz | Fastqsanger.gz | |||
| SRR17461310_1.fastq.gz | Fastqsanger.gz |
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Project description:Purpose: To generate a reference long-read transcriptomic data set for use in developing new analysis pipelines and comparing their performance with existing methods. Synthetic “sequin” RNA standards (Hardwick et al. 2016) were sequenced using the Oxford Nanopore Technologies (ONT) GridION platform.
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Project description:We examined 36 biopsies taken from digital dermatitis lesions of Holstein cows. The target was the V3 -V4 variable region of 16S rRNA using Treponema specific primers. We identified 20 different taxa of Treponema using this approach. Phylogenetic study of the Treponema taxa found in digital dermatitis lesions of Holstein cows.
2013-06-25 | E-GEOD-42426 | biostudies-arrayexpress
Project description:Purpose: To identify the genetic basis of posterior polymorphous corneal dystrophy 1 (PPCD1). Methods: Next-generation sequencing was performed on DNA samples from 4 affected and 4 unaffected members of a previously reported family with PPCD1 linked to chromosome 20 between D20S182 and D20S195. Custom capture probes were utilized for targeted region capture of the linked interval. Single nucleotide variants (SNVs) and insertions/deletions (indels) were identified using two bioinformatics pipelines and two annotation databases. Candidate variants met the following criteria: quality score ≥20, read depth ≥5X, heterozygous, novel or rare (minor allele frequency (MAF) ≤ 0.05), present in each affected individual and absent in each unaffected individual. Structural variants were detected with two different microarray platforms to identify indels of varying sizes. Results: Sequencing reads aligned to the linked region on chromosome 20, and high coverage was obtained across the sequenced region. The majority of identified variants were detected with both pipelines and annotation databases, although unique variants were identified. Twelve SNVs in 10 genes (2 synonymous variants and 10 noncoding variants) and 9 indels in 7 genes met the filtering criteria and were considered candidate variants for PPCD1. Conclusions: Next-generation sequencing of the PPCD1 interval has identified 17 genes containing novel or rare SNVs and indels that segregate with the affected phenotype in an affected family previously mapped to the PPCD1 locus. We anticipate that screening of these candidate genes in other families previously mapped to the PPCD1 locus will result in the identification of the genetic basis of PPCD1.
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Project description:unveiling the hidden repertoire of chemoreceptors from rare taxa in microbial communities
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Project description:Purpose: To identify the genetic basis of posterior polymorphous corneal dystrophy 1 (PPCD1). Methods: Next-generation sequencing was performed on DNA samples from 4 affected and 4 unaffected members of a previously reported family with PPCD1 linked to chromosome 20 between D20S182 and D20S195. Custom capture probes were utilized for targeted region capture of the linked interval. Single nucleotide variants (SNVs) and insertions/deletions (indels) were identified using two bioinformatics pipelines and two annotation databases. Candidate variants met the following criteria: quality score â¥20, read depth â¥5X, heterozygous, novel or rare (minor allele frequency (MAF) ⤠0.05), present in each affected individual and absent in each unaffected individual. Structural variants were detected with two different microarray platforms to identify indels of varying sizes. Results: Sequencing reads aligned to the linked region on chromosome 20, and high coverage was obtained across the sequenced region. The majority of identified variants were detected with both pipelines and annotation databases, although unique variants were identified. Twelve SNVs in 10 genes (2 synonymous variants and 10 noncoding variants) and 9 indels in 7 genes met the filtering criteria and were considered candidate variants for PPCD1. Conclusions: Next-generation sequencing of the PPCD1 interval has identified 17 genes containing novel or rare SNVs and indels that segregate with the affected phenotype in an affected family previously mapped to the PPCD1 locus. We anticipate that screening of these candidate genes in other families previously mapped to the PPCD1 locus will result in the identification of the genetic basis of PPCD1. Four affected and 4 unaffected individuals from a single family were analyzed for copy number variation within the PPCD1 disease locus. Array design and analysis is based on genome build hg19.
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Project description:Youfang Cao & Jie Liang. Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method. The Journal of Chemical Physics 139, 2 (2013).
Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.
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Project description:Youfang Cao & Jie Liang. Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method. The Journal of Chemical Physics 139, 2 (2013).
Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.
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Project description:F. Schl�gl. Chemical reaction models for non-equilibrium phase transitions. Zeitschrift f�r Physik 253, 2 (1972).
Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively by comparing simulation results with true answers. Overall, ABSIS can accurately and efficiently estimate rare event probabilities for all examples, often with smaller variance than other importance sampling algorithms. The ABSIS method is general and can be applied to study rare events of other stochastic networks with complex probability landscape.
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