Daniel Mendoza

Bio: Daniel Mendoza is an academic researcher from University of Cuenca. The author has contributed to research in topics: Oligonucleotide & Polymerase chain reaction. The author has an hindex of 4, co-authored 11 publications receiving 35 citations.

A robust targeted sequencing approach for low input and variable quality DNA from clinical samples.

Abstract: Next-generation deep sequencing of gene panels is being adopted as a diagnostic test to identify actionable mutations in cancer patient samples. However, clinical samples, such as formalin-fixed, paraffin-embedded specimens, frequently provide low quantities of degraded, poor quality DNA. To overcome these issues, many sequencing assays rely on extensive PCR amplification leading to an accumulation of bias and artifacts. Thus, there is a need for a targeted sequencing assay that performs well with DNA of low quality and quantity without relying on extensive PCR amplification. We evaluate the performance of a targeted sequencing assay based on Oligonucleotide Selective Sequencing, which permits the enrichment of genes and regions of interest and the identification of sequence variants from low amounts of damaged DNA. This assay utilizes a repair process adapted to clinical FFPE samples, followed by adaptor ligation to single stranded DNA and a primer-based capture technique. Our approach generates sequence libraries of high fidelity with reduced reliance on extensive PCR amplification-this facilitates the accurate assessment of copy number alterations in addition to delivering accurate single nucleotide variant and insertion/deletion detection. We apply this method to capture and sequence the exons of a panel of 130 cancer-related genes, from which we obtain high read coverage uniformity across the targeted regions at starting input DNA amounts as low as 10 ng per sample. We demonstrate the performance using a series of reference DNA samples, and by identifying sequence variants in DNA from matched clinical samples originating from different tissue types.

A causal flow approach for the evaluation of global climate models

Abstract: © 2020 Royal Meteorological Society Global climate models (GCMs) are generally used to forecast weather, understand the present climate, and project climate change. Their reliability usually rests on their capability to represent climatic processes, and most evaluations directly measure the spatiotemporal agreement of scalar climate variables. However, climate naturally involves complex interactions that are hard to infer and, therefore, difficult to evaluate. Climate networks (CNs) have been used to infer flows of mass and energy in the complex climate system. Here, an Evaluation of Models by Causal Flows (EMCaF) is proposed. EMCaF focuses on the assessment of properties about mass and energy flows in the CNs derived from GCMs. First, causal CNs are inferred from GCMs, and then the capabilities to reproduce characteristic transfer flows are assessed with reference models. A more in-depth feature is the possibility to assess how climate change disturbs CNs properties. In addition to the quantitative difference between modelled and observed values taken into account in standard evaluations, the EMCaF approach aims to assess the weaknesses and strengths of GCMs to represent climate mechanisms and processes that couple different components of the climate system. The comparison of models through this approach allows having complimentary feedback on model evaluations to understand possible causes of errors and enable a judgement based on processes. The approach is illustrated by evaluating one GCM and subsequently assessing changes of its CNs under future climate projections. Results show that known climatic patterns are assimilated and that causal strength patterns are likely to agree with the wind magnitude as a transfer factor. Significative issues are then explored, showing the capabilities of the approach and allowing understand fundamental structures in transport flows, compare their properties, and assess changes in the future. Different alternatives and considerations in each step of the approach are discussed to expand its applicability.

A Robust Targeted Sequencing Approach For Low Input And Variable Quality DNA From Clinical Samples

Abstract: Next-generation sequencing is being adopted as a diagnostic test to identify actionable mutations in cancer patient samples. However, clinical samples such as formalin-fixed, paraffin embedded specimens frequently provide low quantities of degraded, poor quality DNA. To overcome these issues, many sequencing assays rely on extensive PCR amplification leading to an accumulation of bias and artifacts. Thus, there is a need for a targeted sequencing assay that performs well with DNA of low quality and quantity without relying on extensive PCR amplification. We evaluated the performance of a targeted sequencing assay based on Oligonucleotide Selective Sequencing. This assay enables on to sequence and call variants from low amounts of damaged DNA. This assay utilizes a repair process developed to sequence clinical FFPE samples, followed by adaptor ligation to single stranded DNA and a primer-based capture technique. This approach generates sequence libraries of high fidelity without relying heavily on PCR amplification, and facilitates the assessment of copy number alterations across the target regions. Using an assay designed to capture the exons of a panel of 130 actionable cancer genes, we obtain an on-target rate of >50% and high uniformity across targeted regions at starting input DNA amounts of 10ng per sample. We demonstrate the performance of this targeted sequencing assay using a series of reference DNA samples, and in variant identification from low quality DNA samples originating from different tissue types.

Finding teleconnections from decomposed rainfall signals using dynamic harmonic regressions: a Tropical Andean case study

Abstract: Global climate is a multi-scale system whose subsystems interact complexly. Notably, the Tropical-Andean region has a strong rainfall variability because of the confluence of many global climate processes altered by morphological features. An approach for a synthetical climate description is the use of global indicators and their regional teleconnections. However, typically this is carried out using filters and correlations, which results in seasonal and inter-annual teleconnections information, which are difficult to integrate into a modeling framework. A new methodology, based on rainfall signal extraction using dynamic-harmonic-regressions (DHR) and stochastic-multiple-linear-regressions (SMLR) between rainfall components and global signals for searching intra-annual and inter-annual teleconnections, is proposed. DHR gives non-stationary inter-annual trends and intra-annual quasi-periodic oscillations for monthly rainfall measurements. Time-variable amplitudes of quasi-periodical oscillations are crucial for finding intra-annual teleconnections using SMLR, while trends are better suited for the case of inter-annual ones. The methodology is tested over a Tropical-Andean region in southern Ecuador. The following results were obtained: (1) trans-NiA±o-Index (TNI) and Tropical-South-Atlantic signals are strongly connected to inter-annual and intra-annual time-scales. (2) However, TNI progressively weakens its relation with intra-annual components; meanwhile, El-NiA±o-Southern-Oscillation 3 gains ground for such time-scales. (3) Finally, an inter-annual connection with the North-Atlantic-Oscillation (NAO) is revealed. These results are consistent with previous literature, although the TNI and NAO connections are interesting findings, taking into account the differences in the connected scales. These results show the methodology’s capability of unraveling global teleconnections in different space and time scales using attributes embedded in an integral mathematical framework, which could be interesting for other purposes—such as the analysis of climate mechanisms or climate modeling. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

Groundwater level prediction in arid areas using wavelet analysis and Gaussian process regression

Abstract: Utilizing new approaches to accurately predict groundwater level (GWL) in arid regions is of vital importance. In this study, support vector regression (SVR), Gaussian process regression (GPR), and...

MutSignatures: an R package for extraction and analysis of cancer mutational signatures

Abstract: Cancer cells accumulate somatic mutations as result of DNA damage, inaccurate repair and other mechanisms. Different genetic instability processes result in characteristic non-random patterns of DNA mutations, also known as mutational signatures. We developed mutSignatures, an integrated R-based computational framework aimed at deciphering DNA mutational signatures. Our software provides advanced functions for importing DNA variants, computing mutation types, and extracting mutational signatures via non-negative matrix factorization. Specifically, mutSignatures accepts multiple types of input data, is compatible with non-human genomes, and supports the analysis of non-standard mutation types, such as tetra-nucleotide mutation types. We applied mutSignatures to analyze somatic mutations found in smoking-related cancer datasets. We characterized mutational signatures that were consistent with those reported before in independent investigations. Our work demonstrates that selected mutational signatures correlated with specific clinical and molecular features across different cancer types, and revealed complementarity of specific mutational patterns that has not previously been identified. In conclusion, we propose mutSignatures as a powerful open-source tool for detecting the molecular determinants of cancer and gathering insights into cancer biology and treatment.

A robust internal control for high-precision DNA methylation analyses by droplet digital PCR.

Abstract: Droplet digital PCR (ddPCR) allows absolute quantification of nucleic acids and has potential for improved non-invasive detection of DNA methylation. For increased precision of the methylation analysis, we aimed to develop a robust internal control for use in methylation-specific ddPCR. Two control design approaches were tested: (a) targeting a genomic region shared across members of a gene family and (b) combining multiple assays targeting different pericentromeric loci on different chromosomes. Through analyses of 34 colorectal cancer cell lines, the performance of the control assay candidates was optimized and evaluated, both individually and in various combinations, using the QX200™ droplet digital PCR platform (Bio-Rad). The best-performing control was tested in combination with assays targeting methylated CDO1, SEPT9, and VIM. A 4Plex panel consisting of EPHA3, KBTBD4, PLEKHF1, and SYT10 was identified as the best-performing control. The use of the 4Plex for normalization reduced the variability in methylation values, corrected for differences in template amount, and diminished the effect of chromosomal aberrations. Positive Droplet Calling (PoDCall), an R-based algorithm for standardized threshold determination, was developed, ensuring consistency of the ddPCR results. Implementation of a robust internal control, i.e., the 4Plex, and an algorithm for automated threshold determination, PoDCall, in methylation-specific ddPCR increase the precision of DNA methylation analysis.