Open Access Highly Accessed Research article

A genomic scale map of genetic diversity in Trypanosoma cruzi

Alejandro A Ackermann, Leonardo G Panunzi, Raul O Cosentino, Daniel O Sánchez and Fernán Agüero*

Author Affiliations

Instituto de Investigaciones Biotecnológicas - Instituto Tecnológico de Chascomús (IIB-INTECH), Universidad Nacional de San Martín - Consejo de Investigaciones Científicas y Técnicas (UNSAM-CONICET), Sede San Martín, B 1650 HMP, San Martín, Buenos Aires, Argentina

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BMC Genomics 2012, 13:736  doi:10.1186/1471-2164-13-736

Published: 27 December 2012

Additional files

Additional file 1:

Table S1. Complete list of alignments used for the detection of SNPs in this work. The table contains information on the alignments and the type and number of SNPs identified in each case. The Calc/Excel workbook contains two spreadhseets: the first contains a complete legend, describing all columns in detail; the second contains the data itself.

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Additional file 2:

Table S2. Experimental validation of identified SNPs by PCR-based re-sequencing. The table shows, for each loci analyzed, the number of SNPs identified in silico, as well as those that have been validated (V) or not (NV) by re-sequencing.

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Additional file 3:

Table S3. List of nonsense polymorphisms identified in this work. For each nonsense SNP identified we show the corresponding codon for each genome/strain analyzed, and N/A if the gene has not been identified in a dataset.

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Additional file 4:

Figure S1. Nonsense SNPs observed in the CL-Brener genome. (A) List of observed nonsense SNPs, sorted by descending frequency. Nonsense changes shown in black require only a single mutation to produce a stop codon. SNPs shown in color require changing two bases in the same codon (Ts = transition; Tv = transversion). (B) List of observed nonsense mutations, by type (Ts/Tv). (C) Genetic code showing all possible single base changes that would produce a stop codon, and those observed in T. cruzi.

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Additional file 5:

Figure S2. SNP density in globular vs unstructured protein domains. The density of SNPs in globular vs intrinsically unstructured regions (predicted by IUPred) was compared for synonymous, and non-synonymous changes (left and middle panels). A third panel (right) showing all SNPs is shown for comparison. In all cases the differences between the distribution were not statistically different.

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Additional file 6:

Figure S3. A natively unstructured domain accumulating non-synonymous changes (complete alignment). Multiple sequence alignment showing the TcCLB.506553.20 gene, and its allelic counterparts in other strains (Additional file 10: Figure S3). The gene has a globular N-terminal domain, and an intrinsically unstructured C-terminal domain, as predicted by IUPred [73]. This figure contains the complete alignment shown in Figure  1.

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Additional file 7:

Table S4. GO Slim annotation of alignments used for the detection of SNPs in this work.

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Additional file 8:

Table S5. KEGG pathway mapping of alignments used for the detection of SNPs in this work.

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Additional file 9:

Table S6. Drug target prioritization using a score based on the nucleotide diversity estimator (π) as one of the main driving criteria. The spreadhsheet contains the top 300 targets. The complete ranked proteome, as well as the individual queries can be accessed online at http://tdrtargets.org/published/browse/t/423.

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Additional file 10:

Table S7. Primers and amplification products used in the SNP validation experiment.

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