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ANDES: Statistical tools for the ANalyses of DEep Sequencing.

Li K, Venter E, Yooseph S, Stockwell TB, Eckerle LD, Denison MR, Spiro DJ, Methé BA - BMC Res Notes (2010)

Bottom Line: Tools include the root mean square deviation (RMSD) plot, which allows for the visual comparison of multiple samples on a position-by-position basis, and the computation of base conversion frequencies (transition/transversion rates), variation (Shannon entropy), inter-sample clustering and visualization (dendrogram and multidimensional scaling (MDS) plot), threshold-driven consensus sequence generation and polymorphism detection, and the estimation of empirically determined sequencing quality values.As new sequencing technologies evolve, deep sequencing will become increasingly cost-efficient and the inter and intra-sample comparisons of largely homogeneous sequences will become more common.We have provided a software package and demonstrated its application on various empirically-derived datasets.

View Article: PubMed Central - HTML - PubMed

Affiliation: The J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. kli@jcvi.org.

ABSTRACT

Background: The advancements in DNA sequencing technologies have allowed researchers to progress from the analyses of a single organism towards the deep sequencing of a sample of organisms. With sufficient sequencing depth, it is now possible to detect subtle variations between members of the same species, or between mixed species with shared biomarkers, such as the 16S rRNA gene. However, traditional sequencing analyses of samples from largely homogeneous populations are often still based on multiple sequence alignments (MSA), where each sequence is placed along a separate row and similarities between aligned bases can be followed down each column. While this visual format is intuitive for a small set of aligned sequences, the representation quickly becomes cumbersome as sequencing depths cover loci hundreds or thousands of reads deep.

Findings: We have developed ANDES, a software library and a suite of applications, written in Perl and R, for the statistical ANalyses of DEep Sequencing. The fundamental data structure underlying ANDES is the position profile, which contains the nucleotide distributions for each genomic position resultant from a multiple sequence alignment (MSA). Tools include the root mean square deviation (RMSD) plot, which allows for the visual comparison of multiple samples on a position-by-position basis, and the computation of base conversion frequencies (transition/transversion rates), variation (Shannon entropy), inter-sample clustering and visualization (dendrogram and multidimensional scaling (MDS) plot), threshold-driven consensus sequence generation and polymorphism detection, and the estimation of empirically determined sequencing quality values.

Conclusions: As new sequencing technologies evolve, deep sequencing will become increasingly cost-efficient and the inter and intra-sample comparisons of largely homogeneous sequences will become more common. We have provided a software package and demonstrated its application on various empirically-derived datasets. Investigators may download the software from Sourceforge at https://sourceforge.net/projects/andestools.

No MeSH data available.


Example effect of percentage filtering used to reduce the coverage of ambiguity codes. In the original nucleotide distribution for a specific position, the ambiguity code D, which represents A, G, or T is called. C is likely to be a result of sequencing error, so it is preferable to exclude it when designing degenerate primers. After applying a 5% filter, the assigned ambiguity code for that position can be reduced to a W, which more accurately represents the target population (A or T).
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Figure 7: Example effect of percentage filtering used to reduce the coverage of ambiguity codes. In the original nucleotide distribution for a specific position, the ambiguity code D, which represents A, G, or T is called. C is likely to be a result of sequencing error, so it is preferable to exclude it when designing degenerate primers. After applying a 5% filter, the assigned ambiguity code for that position can be reduced to a W, which more accurately represents the target population (A or T).

Mentions: An example of the effect of filtering based on nucleotide probability is shown in Figure 7. In this example, the ambiguity code assigned to the position was D, because the position required representation for the existence of A, T, and C in the sample. A sequencing error upper bound rate of 5% was determined and used as a threshold for filtering low prevalence nucleotide representations. The necessity for a C representation was removed, allowing the new ambiguity code to become a W, representing A or T. Assuming the representation of C was a result of sequencing error, the W ambiguity code is a more accurate representation of the allelic composition at that position for the sample.


ANDES: Statistical tools for the ANalyses of DEep Sequencing.

Li K, Venter E, Yooseph S, Stockwell TB, Eckerle LD, Denison MR, Spiro DJ, Methé BA - BMC Res Notes (2010)

Example effect of percentage filtering used to reduce the coverage of ambiguity codes. In the original nucleotide distribution for a specific position, the ambiguity code D, which represents A, G, or T is called. C is likely to be a result of sequencing error, so it is preferable to exclude it when designing degenerate primers. After applying a 5% filter, the assigned ambiguity code for that position can be reduced to a W, which more accurately represents the target population (A or T).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2921379&req=5

Figure 7: Example effect of percentage filtering used to reduce the coverage of ambiguity codes. In the original nucleotide distribution for a specific position, the ambiguity code D, which represents A, G, or T is called. C is likely to be a result of sequencing error, so it is preferable to exclude it when designing degenerate primers. After applying a 5% filter, the assigned ambiguity code for that position can be reduced to a W, which more accurately represents the target population (A or T).
Mentions: An example of the effect of filtering based on nucleotide probability is shown in Figure 7. In this example, the ambiguity code assigned to the position was D, because the position required representation for the existence of A, T, and C in the sample. A sequencing error upper bound rate of 5% was determined and used as a threshold for filtering low prevalence nucleotide representations. The necessity for a C representation was removed, allowing the new ambiguity code to become a W, representing A or T. Assuming the representation of C was a result of sequencing error, the W ambiguity code is a more accurate representation of the allelic composition at that position for the sample.

Bottom Line: Tools include the root mean square deviation (RMSD) plot, which allows for the visual comparison of multiple samples on a position-by-position basis, and the computation of base conversion frequencies (transition/transversion rates), variation (Shannon entropy), inter-sample clustering and visualization (dendrogram and multidimensional scaling (MDS) plot), threshold-driven consensus sequence generation and polymorphism detection, and the estimation of empirically determined sequencing quality values.As new sequencing technologies evolve, deep sequencing will become increasingly cost-efficient and the inter and intra-sample comparisons of largely homogeneous sequences will become more common.We have provided a software package and demonstrated its application on various empirically-derived datasets.

View Article: PubMed Central - HTML - PubMed

Affiliation: The J, Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA. kli@jcvi.org.

ABSTRACT

Background: The advancements in DNA sequencing technologies have allowed researchers to progress from the analyses of a single organism towards the deep sequencing of a sample of organisms. With sufficient sequencing depth, it is now possible to detect subtle variations between members of the same species, or between mixed species with shared biomarkers, such as the 16S rRNA gene. However, traditional sequencing analyses of samples from largely homogeneous populations are often still based on multiple sequence alignments (MSA), where each sequence is placed along a separate row and similarities between aligned bases can be followed down each column. While this visual format is intuitive for a small set of aligned sequences, the representation quickly becomes cumbersome as sequencing depths cover loci hundreds or thousands of reads deep.

Findings: We have developed ANDES, a software library and a suite of applications, written in Perl and R, for the statistical ANalyses of DEep Sequencing. The fundamental data structure underlying ANDES is the position profile, which contains the nucleotide distributions for each genomic position resultant from a multiple sequence alignment (MSA). Tools include the root mean square deviation (RMSD) plot, which allows for the visual comparison of multiple samples on a position-by-position basis, and the computation of base conversion frequencies (transition/transversion rates), variation (Shannon entropy), inter-sample clustering and visualization (dendrogram and multidimensional scaling (MDS) plot), threshold-driven consensus sequence generation and polymorphism detection, and the estimation of empirically determined sequencing quality values.

Conclusions: As new sequencing technologies evolve, deep sequencing will become increasingly cost-efficient and the inter and intra-sample comparisons of largely homogeneous sequences will become more common. We have provided a software package and demonstrated its application on various empirically-derived datasets. Investigators may download the software from Sourceforge at https://sourceforge.net/projects/andestools.

No MeSH data available.