Prediction of PCR amplification from primer and template sequences using recurrent neural network

A computational framework to improve cross-platform implementation of transcriptomics signatures

The emergence of next-generation sequencing technologies and computational advances have expanded our understanding of gene expression regulation (i.e., the transcriptome). This has also led to an increased interest in using transcriptomic biomarkers to improve disease diagnosis and stratification, to assess prognosis and predict the response to treatment. Significant progress in identifying transcriptomic signatures for various clinical needs has been made, with large discovery studies accounting for challenges such as patient variability, unwanted batch effects, and data complexities; however, obstacles related to the technical aspects of cross-platform implementation still hinder the successful integration of transcriptomic technologies into standard diagnostic workflows. In this article, we discuss the challenges associated with integrating transcriptomic signatures derived using high-throughput technologies (such as RNA-sequencing) into clinical diagnostic tools using nucleic acid amplification (NAA) techniques. The novelty of the proposed approach lies in our aim to embed constraints related to cross-platform implementation in the process of signature discovery. These constraints could include technical limitations of amplification platform and chemistry, the maximal number of targets imposed by the chosen multiplexing strategy, and the genomic context of identified RNA biomarkers. Finally, we propose to build a computational framework that would integrate these constraints in combination with existing statistical and machine learning models used for signature identification. We envision that this could accelerate the integration of RNA signatures discovered by high-throughput technologies into NAA-based approaches suitable for clinical applications.

Efficient DNA Coding Algorithm for Polymerase Chain Reaction Amplification Information Retrieval

Polymerase Chain Reaction (PCR) amplification is widely used for retrieving information from DNA storage. During the PCR amplification process, nonspecific pairing between the 3' end of the primer and the DNA sequence can cause cross-talk in the amplification reaction, leading to the generation of interfering sequences and reduced amplification accuracy. To address this issue, we propose an efficient coding algorithm for PCR amplification information retrieval (ECA-PCRAIR). This algorithm employs variable-length scanning and pruning optimization to construct a codebook that maximizes storage density while satisfying traditional biological constraints. Subsequently, a codeword search tree is constructed based on the primer library to optimize the codebook, and a variable-length interleaver is used for constraint detection and correction, thereby minimizing the likelihood of nonspecific pairing. Experimental results demonstrate that ECA-PCRAIR can reduce the probability of nonspecific pairing between the 3' end of the primer and the DNA sequence to 2-25%, enhancing the robustness of the DNA sequences. Additionally, ECA-PCRAIR achieves a storage density of 2.14-3.67 bits per nucleotide (bits/nt), significantly improving storage capacity.

Environmental DNA: The next chapter

Molecular tools are an indispensable part of ecology and biodiversity sciences and implemented across all biomes. About a decade ago, the use and implementation of environmental DNA (eDNA) to detect biodiversity signals extracted from environmental samples opened new avenues of research. Initial eDNA research focused on understanding population dynamics of target species. Its scope thereafter broadened, uncovering previously unrecorded biodiversity via metabarcoding in both well-studied and understudied ecosystems across all taxonomic groups. The application of eDNA rapidly became an established part of biodiversity research, and a research field by its own. Here, we revisit key expectations made in a land-mark special issue on eDNA in Molecular Ecology in 2012 to frame the development in six key areas: (1) sample collection, (2) primer development, (3) biomonitoring, (4) quantification, (5) behaviour of DNA in the environment and (6) reference database development. We pinpoint the success of eDNA, yet also discuss shortfalls and expectations not met, highlighting areas of research priority and identify the unexpected developments. In parallel, our retrospective couples a screening of the peer-reviewed literature with a survey of eDNA users including academics, end-users and commercial providers, in which we address the priority areas to focus research efforts to advance the field of eDNA. With the rapid and ever-increasing pace of new technical advances, the future of eDNA looks bright, yet successful applications and best practices must become more interdisciplinary to reach its full potential. Our retrospect gives the tools and expectations towards concretely moving the field forward.

DeGenPrime provides robust primer design and optimization unlocking the biosphere

Motivation

Polymerase chain reaction (PCR) is the world's most important molecular diagnostic with applications ranging from medicine to ecology. PCR can fail because of poor primer design. The nearest-neighbor thermodynamic properties, picking conserved regions, and filtration via penalty of oligonucleotides form the basis for good primer design.

Results

DeGenPrime is a console-based high-quality PCR primer design tool that can utilize MSA formats and degenerate bases expanding the target range for a single primer set. Our software utilizes thermodynamic properties, filtration metrics, penalty scoring, and conserved region finding of any proposed primer. It has degeneracy, repeated k-mers, relative GC content, and temperature range filters. Minimal penalty scoring is included according to secondary structure self-dimerization metrics, GC clamping, tri- and tetra-loop hairpins, and internal repetition. We compared PrimerDesign-M, DegePrime, ConsensusPrimer, and DeGenPrime on acceptable primer yield. PrimerDesign-M, DegePrime, and ConsensusPrimer provided 0%, 11%, and 17% yield, respectively, for the alternative iron nitrogenase (anfD) gene target. DeGenPrime successfully identified quality primers within the conserved regions of the T4-like phage major capsid protein (g23), conserved regions of molybdenum-based nitrogenase (nif), and its alternatives vanadium (vnf) and iron (anf) nitrogenase. DeGenPrime provides a universal and scalable primer design tool for the entire tree of life.

Availability and implementation

DeGenPrime is written in C++ and distributed under a BSD-3-Clause license. The source code for DeGenPrime is freely available on www.github.com/raw-lab/degenprime.

Mixed insect pest populations of Diaspididae species under control of oligonucleotide insecticides: 3'-end nucleotide matters

Diaspididae are one of the most serious small herbivorous insects with piercing-sucking mouth parts and are major economic pests as they attack and destroy perennial ornamentals and food crops. Chemical control is the primary management approach for armored scale infestation. However, chemical insecticides do not possess selectivity in action and not always effective enough for the control of armored scale insects. Our previous work showed that green oligonucleotide insecticides (olinscides) are highly effective against armored and soft scale insects. Moreover, olinscides possess affordability, selectivity in action, fast biodegradability, and a low carbon footprint. Insect pest populations undergo microevolution and olinscides should take into account the problem of insecticide resistance. Using sequencing results, it was found that in the mixed populations of insect pests Dynaspidiotus britannicus Newstead and Aonidia lauri Bouche, predominates the population of A. lauri. Individuals of A. lauri comprised for 80% of individuals with the sequence 3'-ATC-GTT-GGC-AT-5' in the 28S rRNA site, and 20% of the population comprised D. britannicus individuals with the sequence 3'-ATC-GTC-GGT-AT-5'. We created olinscides Diasp80-11 (5'-ATG-CCA-ACG-AT-3') and Diasp20-11 (5'-ATA-CCG-ACG-AT-3') with perfect complementarity to each of the sequences. Mortality of insects on the 14th day comprised 98.19 ± 3.12% in Diasp80-11 group, 64.66 ± 0.67% in Diasp20-11 group (p < 0.05), and 3.77 ± 0.94% in the control group. Results indicate that for maximum insecticidal effect it is necessary to use an oligonucleotide insecticide that corresponds to the dominant species. Mortality in Diasp80-11 group was accompanied with significant decrease in target 28S rRNA concentration and was 8.44 ± 0.14 and 1.72 ± 0.36 times lower in comparison with control (p < 0.05) on the 10th and 14th days, respectively. We decided to make single nucleotide substitutions in Diasp20-11 olinscide to understand which nucleotide will play the most important role in insecticidal effect. We created three sequences with single nucleotide transversion substitutions at the 5'-end - Diasp20(5')-11 (A to T), 3'-end - Diasp20(3')-11 (T to A), and in the middle of the sequence - Diasp20(6)-11 (6th nitrogenous base of the sequence; G to C), respectively. As a result, mortality of mixed population of the field experiment decreased and comprised 53.89 ± 7.25% in Diasp20(5')-11 group, 40.68 ± 4.33% in Diasp20(6)-11 group, 35.74 ± 5.51% in Diasp20(3')-11 group, and 3.77 ± 0.94% in the control group on the 14th day. Thus, complementarity of the 3'-end nucleotide to target 28S rRNA was the most important for pronounced insecticidal effect (significance of complementarity of nucleotides for insecticidal effect: 5' nt < 6 nt < 3' nt). As was found in our previous research works, the most important rule to obtain maximum insecticidal effect is complete complementarity to the target rRNA sequence and maximum coverage of target sequence in insect pest populations. However, in this article we also show that the complementarity of 3'-end is a second important factor for insecticidal potential of olinscides. Also in this article we propose 2-step DNA containment mechanism of action of olinscides, recruiting RNase H. The data obtained indicate the selectivity of olinscides and at the same time provide a simple and flexible platform for the creation of effective plant protection products, based on antisense DNA oligonucleotides.

High throughput human genotyping for variants associated with malarial disease outcomes using custom targeted amplicon sequencing

Malaria has exhibited the strongest known selective pressure on the human genome in recent history and is the evolutionary driving force behind genetic conditions, such as sickle-cell disease, glucose-6-phosphatase deficiency, and some other erythrocyte defects. Genomic studies (e.g., The 1000 Genomes project) have provided an invaluable baseline for human genetics, but with an estimated two thousand ethno-linguistic groups thought to exist across the African continent, our understanding of the genetic differences between indigenous populations and their implications on disease is still limited. Low-cost sequencing-based approaches make it possible to target specific molecular markers and genes of interest, leading to potential insights into genetic diversity. Here we demonstrate the versatility of custom dual-indexing technology and Illumina next generation sequencing to generate a genetic profile of human polymorphisms associated with malaria pathology. For 100 individuals diagnosed with severe malaria in Northeast Tanzania, variants were successfully characterised on the haemoglobin subunit beta (HBB), glucose-6-phosphate dehydrogenase (G6PD), atypical chemokine receptor 1 (ACKR1) genes, and the intergenic Dantu genetic blood variant, then validated using pre-existing genotyping data. High sequencing coverage was observed across all amplicon targets in HBB, G6PD, ACKR1, and the Dantu blood group, with variants identified at frequencies previously observed within this region of Tanzania. Sequencing data exhibited high concordance rates to pre-existing genotyping data (> 99.5%). Our work demonstrates the potential utility of amplicon sequencing for applications in human genetics, including to personalise medicine and understand the genetic diversity of loci linked to important host phenotypes, such as malaria susceptibility.

A comparative study of three models to analyze the impact of air pollutants on the number of pulmonary tuberculosis cases in Urumqi, Xinjiang

In this paper, we separately constructed ARIMA, ARIMAX, and RNN models to determine whether there exists an impact of the air pollutants (such as PM2.5, PM10, CO, O3, NO2, and SO2) on the number of pulmonary tuberculosis cases from January 2014 to December 2018 in Urumqi, Xinjiang. In addition, by using a new comprehensive evaluation index DISO to compare the performance of three models, it was demonstrated that ARIMAX (1,1,2) × (0,1,1)12 + PM2.5 (lag = 12) model was the optimal one, which was applied to predict the number of pulmonary tuberculosis cases in Urumqi from January 2019 to December 2019. The predicting results were in good agreement with the actual pulmonary tuberculosis cases and shown that pulmonary tuberculosis cases obviously declined, which indicated that the policies of environmental protection and universal health checkups in Urumqi have been very effective in recent years.

AI-aided on-chip nucleic acid assay for smart diagnosis of infectious disease

Global pandemics such as COVID-19 have resulted in significant global social and economic disruption. Although polymerase chain reaction (PCR) is recommended as the standard test for identifying the SARS-CoV-2, conventional assays are time-consuming. In parallel, although artificial intelligence (AI) has been employed to contain the disease, the implementation of AI in PCR analytics, which may enhance the cognition of diagnostics, is quite rare. The information that the amplification curve reveals can reflect the dynamics of reactions. Here, we present a novel AI-aided on-chip approach by integrating deep learning with microfluidic paper-based analytical devices (µPADs) to detect synthetic RNA templates of the SARS-CoV-2 ORF1ab gene. The µPADs feature a multilayer structure by which the devices are compatible with conventional PCR instruments. During analysis, real-time PCR data were synchronously fed to three unsupervised learning models with deep neural networks, including RNN, LSTM, and GRU. Of these, the GRU is found to be most effective and accurate. Based on the experimentally obtained datasets, qualitative forecasting can be made as early as 13 cycles, which significantly enhances the efficiency of the PCR tests by 67.5% (∼40 min). Also, an accurate prediction of the end-point value of PCR curves can be obtained by GRU around 20 cycles. To further improve PCR testing efficiency, we also propose AI-aided dynamic evaluation criteria for determining critical cycle numbers, which enables real-time quantitative analysis of PCR tests. The presented approach is the first to integrate AI for on-chip PCR data analysis. It is capable of forecasting the final output and the trend of qPCR in addition to the conventional end-point Cq calculation. It is also capable of fully exploring the dynamics and intrinsic features of each reaction. This work leverages methodologies from diverse disciplines to provide perspectives and insights beyond the scope of a single scientific field. It is universally applicable and can be extended to multiple areas of fundamental research.