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The hidden treasure in your data: phasing with unexpected weak anomalous scatterers from routine data sets.

ABSTRACT: Single-wavelength anomalous dispersion (SAD) utilizing anomalous signal from native S atoms, or other atoms with Z ? 20, generally requires highly redundant data collected using relatively long-wavelength X-rays. Here, the results from two proteins are presented where the anomalous signal from serendipitously acquired surface-bound Ca atoms with an anomalous data multiplicity of around 10 was utilized to drive de novo structure determination. In both cases, the Ca atoms were acquired from the crystallization solution, and the data-collection strategy was not optimized to exploit the anomalous signal from these scatterers. The X-ray data were collected at 0.98?Å wavelength in one case and at 1.74?Å in the other (the wavelength was optimized for sulfur, but the anomalous signal from calcium was exploited for structure solution). Similarly, using a test case, it is shown that data collected at ?1.0?Å wavelength, where the f'' value for sulfur is 0.28?e, are sufficient for structure determination using intrinsic S atoms from a strongly diffracting crystal. Interestingly, it was also observed that SHELXD was capable of generating a substructure solution from high-exposure data with a completeness of 70% for low-resolution reflections extending to 3.5?Å resolution with relatively low anomalous multiplicity. Considering the fact that many crystallization conditions contain anomalous scatterers such as Cl, Ca, Mn etc., checking for the presence of fortuitous anomalous signal in data from well diffracting crystals could prove useful in either determining the structure de novo or in accurately assigning surface-bound atoms.

SUBMITTER: Hegde RP

PROVIDER: S-EPMC5379167 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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The hidden treasure in your data: phasing with unexpected weak anomalous scatterers from routine data sets.

Hegde Raghurama P RP Fedorov Alexander A AA Sauder J Michael JM Burley Stephen K SK Almo Steven C SC Ramagopal Udupi A UA

Acta crystallographica. Section F, Structural biology communications 20170322 Pt 4

Single-wavelength anomalous dispersion (SAD) utilizing anomalous signal from native S atoms, or other atoms with Z ≤ 20, generally requires highly redundant data collected using relatively long-wavelength X-rays. Here, the results from two proteins are presented where the anomalous signal from serendipitously acquired surface-bound Ca atoms with an anomalous data multiplicity of around 10 was utilized to drive de novo structure determination. In both cases, the Ca atoms were acquired from the cr ...[more]

PMID: 28368276

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Project description:BackgroundNext Generation Sequencing (NGS) experiments produce millions of short sequences that, mapped to a reference genome, provide biological insights at genomic, transcriptomic and epigenomic level. Typically the amount of reads that correctly maps to the reference genome ranges between 70% and 90%, leaving in some cases a consistent fraction of unmapped sequences. This 'misalignment' can be ascribed to low quality bases or sequence differences between the sample reads and the reference genome. Investigating the source of the unmapped reads is definitely important to better assess the quality of the whole experiment and to check for possible downstream or upstream 'contamination' from exogenous nucleic acids.ResultsHere we propose DecontaMiner, a tool to unravel the presence of contaminating sequences among the unmapped reads. It uses a subtraction approach to identify bacteria, fungi and viruses genome contamination. DecontaMiner generates several output files to track all the processed reads, and to provide a complete report of their characteristics. The good quality matches on microorganism genomes are counted and compared among samples. DecontaMiner builds an offline HTML page containing summary statistics and plots. The latter are obtained using the state-of-the-art D3 javascript libraries. DecontaMiner has been mainly used to detect contamination in human RNA-Seq data. The software is freely available at http://www-labgtp.na.icar.cnr.it/decontaminer .ConclusionsDecontaMiner is a tool designed and developed to investigate the presence of contaminating sequences in unmapped NGS data. It can suggest the presence of contaminating organisms in sequenced samples, that might derive either from laboratory contamination or from their biological source, and in both cases can be considered as worthy of further investigation and experimental validation. The novelty of DecontaMiner is mainly represented by its easy integration with the standard procedures of NGS data analysis, while providing a complete, reliable, and automatic pipeline.

Dataset Information

The hidden treasure in your data: phasing with unexpected weak anomalous scatterers from routine data sets.

Publications

The hidden treasure in your data: phasing with unexpected weak anomalous scatterers from routine data sets.

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