Sequences Of Different Lengths Research Articles

Learned Factor Graphs for Inference From Stationary Time Sequences

The design of methods for inference from time sequences has traditionally relied on statistical models that describe the relation between a latent desired sequence and the observed one. A broad family of model-based algorithms have been derived to carry out inference at controllable complexity using recursive computations over the factor graph representing the underlying distribution. An alternative model-agnostic approach utilizes machine learning (ML) methods. Here we propose a framework that combines model-based algorithms and data-driven ML tools for stationary time sequences. In the proposed approach, neural networks are developed to separately learn specific components of a factor graph describing the distribution of the time sequence, rather than the complete inference task. By exploiting stationary properties of this distribution, the resulting approach can be applied to sequences of varying temporal duration. Learned factor graphs can be realized using compact neural networks that are trainable using small training sets, or alternatively, be used to improve upon existing deep inference systems. We present an inference algorithm based on learned stationary factor graphs, which learns to implement the sum-product scheme from labeled data, and can be applied to sequences of different lengths. Our experimental results demonstrate the ability of the proposed learned factor graphs to learn from small training sets to carry out accurate inference for sleep stage detection using the Sleep-EDF dataset, as well as for symbol detection in digital communications with unknown channels.

SaAlign: Multiple DNA/RNA sequence alignment and phylogenetic tree construction tool for ultra-large datasets and ultra-long sequences based on suffix array

Multiple DNA/RNA sequence alignment is an important fundamental tool in bioinformatics, especially for phylogenetic tree construction. With DNA-sequencing improvements, the amount of bioinformatics data is constantly increasing, and various tools need to be iterated constantly. Mitochondrial genome analyses of multiple individuals and species require bioinformatics software; therefore, their performances need to be optimized. To improve the alignment of ultra-large datasets and ultra-long sequences, we optimized a dynamic programming algorithm using longest common substring methods. Ultra-large test DNA datasets, containing sequences of different lengths, some over 300 kb (kilobase), revealed that the Multiple DNA/RNA Sequence Alignment Tool Based on Suffix Tree (SaAlign) saved time and computational space. It outperformed the existing technical tools, including MAFFT and HAlign-II. For mitochondrial genome datasets having limited numbers of sequences, MAFFT performed the required tasks, but it could not handle ultra-large mitochondrial genome datasets for core dump error. We implement a multiple DNA/RNA sequence alignment tool based on Center Star strategy and use suffix array algorithm to optimize the spatial and time efficiency. Nowadays, whole-genome research and NGS technology are becoming more popular, and it is necessary to save computational resources for laboratories. That software is of great significance in these aspects, especially in the study of the whole-mitochondrial genome of plants.

Impact of chaotic dynamics on the performance of metaheuristic optimization algorithms: An experimental analysis

Random mechanisms including mutations are an internal part of evolutionary algorithms, which are based on the fundamental ideas of Darwin’s theory of evolution as well as Mendel’s theory of genetic heritage. In this paper, we debate whether pseudo-random processes are needed for evolutionary algorithms or whether deterministic chaos, which is not a random process, can be suitably used instead. Specifically, we compare the performance of 10 evolutionary algorithms driven by chaotic dynamics and pseudo-random number generators using chaotic processes as a comparative study. In this study, the logistic equation is employed for generating periodical sequences of different lengths, which are used in evolutionary algorithms instead of randomness. We suggest that, instead of pseudo-random number generators, a specific class of deterministic processes (based on deterministic chaos) can be used to improve the performance of evolutionary algorithms. Finally, based on our findings, we propose new research questions.

Targetron-Assisted Delivery of Exogenous DNA Sequences into Pseudomonas putida through CRISPR-Aided Counterselection.

Genome editing methods based on group II introns (known as targetron technology) have long been used as a gene knockout strategy in a wide range of organisms, in a fashion independent of homologous recombination. Yet, their utility as delivery systems has typically been suboptimal due to the reduced efficiency of insertion when carrying exogenous sequences. We show that this limitation can be tackled and targetrons can be adapted as a general tool in Gram-negative bacteria. To this end, a set of broad-host-range standardized vectors were designed for the conditional expression of the Ll.LtrB intron. After establishing the correct functionality of these plasmids in Escherichia coli and Pseudomonas putida, we created a library of Ll.LtrB variants carrying cargo DNA sequences of different lengths, to benchmark the capacity of intron-mediated delivery in these bacteria. Next, we combined CRISPR/Cas9-facilitated counterselection to increase the chances of finding genomic sites inserted with the thereby engineered introns. With these novel tools, we were able to insert exogenous sequences of up to 600 bp at specific genomic locations in wild-type P. putida KT2440 and its ΔrecA derivative. Finally, we applied this technology to successfully tag P. putida with an orthogonal short sequence barcode that acts as a unique identifier for tracking this microorganism in biotechnological settings. These results show the value of the targetron approach for the unrestricted delivery of small DNA fragments to precise locations in the genomes of Gram-negative bacteria, which will be useful for a suite of genome editing endeavors.

Investigation on the competition of duplex/ G-quadruplex/ i-motif in telomere sequences and c-MYC gene with a solid-state nanopore sensor

Apart from duplex helix, the coexisted noncanonical DNA may be involved in inhibiting DNA replication, and controlling gene expression. The competition of duplex/ G4/ i-motif depends on many factors, including sequence information, external conditions and binding effects. This work looked into the human telomere sequences of different length (hTelo-1, 2) and intramolecular G-rich and C-rich strands (hTelo-3, 4, 5). Nanopore translocation properties suggested that in the presence of their complementary sequences in the mixed K+/Cs+ buffer, G4 was the major species, and G4 from hTelo-2 that holds multiple G-quartets formed slower than shorter hTelo-1. The intramolecular G/C-rich strands hTelo-3, 4, 5 predominantly formed duplex rather than quadruplex under K+ or acidic conditions, indicating that intramolecular quadruplex is less stable than duplex due to the strand strain. Translocation behaviors of mixed proto-oncogene cMYC/c-cMYC illustrated that G4 was formed in both K+ and Cs+, and Cs+ inhibited the duplex formation in K+/Cs+ buffer. The increase of the ratio of Cs+ will decrease the duplexes species. Our work demonstrated the relative stability of duplex/quadruplex under various conditions with distinct strands, which is useful for the investigation of DNA secondary structural dynamics and transition mechanism in biological function regions, and paved the way for the potential of disease-targeted diagnosis.

Word Representation Learning Based on Bidirectional GRUs With Drop Loss for Sentiment Classification

Sentiment classification is a fundamental task in many natural language processing applications. Neural networks have achieved great successes on the sentiment classification task in recent years, since recurrent neural networks and long-short-term memory networks have the ability to deal with sequences of different lengths and to capture contextual semantic information. However, the effectiveness of these methods is limited when used to extract contextual information from relatively long texts. Therefore, in our model, we apply bidirectional gated recurrent units to capture contextual information as far as possible when learning word representations, which may effectively reduce the noise compared to other methods. We also propose a novel loss function namely drop loss (DL) which makes the model focus on the hard examples - examples which are easily classified incorrectly - in order to improve the accuracy of the model. We experiment on four commonly used datasets, and the results show that the proposed method has a good performance on four datasets, and needs fewer parameters compared with recent benchmarks, such as CoVe, ULMFiT, embeddings from language models, and bidirectional encoder representations from transformers. Furthermore, we demonstrate that the classification performance of existing shallow network models can be significantly improved by using DL. In particular, the accuracy of the CNN+LSTM model improves 9% on the IMDB-10 dataset.

Segmented-Block Poly(ether amide)s Containing Flexible Polydisperse Polyethyleneoxide Sequences and Rigid Aromatic Amide Moieties.

We describe the synthesis and characterization of three novel aromatic diamines containing oxyethylene sequences of different lengths. These diamines were polymerized using the low-temperature solution polycondensation method with isophthaloyl chloride (IPC), terepthaloyl chloride (TPC), [1,1’-biphenyl]-4,4’-dicarbonyl dichloride (BDC), and 4,4′-oxybis(benzoyl chloride) (OBE), obtaining twelve poly(ether amide)s with short segments of polydisperse polyethyleneoxide (PEO) sequences in the polymer backbone. These polymers show reasonably high molecular mass materials (Mw > 12,000), and the relationship between their structure and properties has been carefully studied. Compared with conventional polyamides containing monodisperse PEO sequences, the polydispersity of the PEO segments within the structural units exerts a significant influence on the crystallinity, flexibility, solubility, and the thermal properties of the polymers. For instance, the all-para oriented polyamides (TPCP-A), with an average number of 8.2 ethylenoxide units per structural unit can be transformed conventionally (Tm = 259 °C) in comparison with thermally untransformable polymer with 2 ethylenoxide units (Tm = 425 °C).

Evaluation of analytical protocols of alignment mapping tools using high throughput next-generation genome sequencing data

Background: Ever since the development of first next-generation genome sequencer (NGS) in 2005, there are rapid developments of high throughput next-generation genome sequencing (HT-NGS) techniques and tools used in genetics and genomics has become much more comfortable and cheaper. The result is the generation of a massive amount of data sets, requiring detailed analysis, which becomes impossible without the use of appropriate bioinformatics tools. One of the crucial steps in the analysis of NGS data is to map readings to a reference sequence. Although the dominance of Illumina synthesis by sequencing (SBS) technology has been noticeable in recent years, the choice of the tools is hampered and the variety of input data and reference genomes. Moreover, the tools used are crucial for result files and further analysis.Methods: The subject of this paper is the three most frequently used alignment mapping programs, which have functions to allow working with many platforms: BWA, Bowtie2 and SMALT. The task of the tested aligners is to match short sequences coming from NGS with reference sequences. The most popular: BWA and Bowtie2 use for this purpose the Burrows-Wheeler transformation and SMALT maps the sequences using hashing and dynamic programming. The presented paper aimed to compare the quality and efficiency of the alignment mapping programs under examination, due to three criteria: i) the quality of the compared sequences of different lengths and from different platforms; ii) coefficient of wrongly compared sequences; iii) the computational resources used.Results: By comparing the results of the mapping analyses for all the programs used, the least popular SMALT is the best. Obtaining the highest percentage of mapped readings for each platform and maintaining the lowest computational memory usage, turns out to be the most optimal choice.Conclusions: The results presented in this paper can be used to verify and rebuild data analysis pipelines from NGS based so far on other tools. We conclude that by using the tools under appropriate conditions, it is possible to improve the quality of the analyses, speed them up and reduce their cost.

Image segmentation encryption algorithm with chaotic sequence generation participated by cipher and multi-feedback loops

The existing chaotic image encryption algorithms have common defects: (i) ciphertext does not participate in the generation processes of chaotic pseudo-random sequences and key sequences; (ii) the entire encryption process does not have a closed-loop structure. In order to solve above problems, in this paper, an image segmentation encryption algorithm based on hyperchaotic system is proposed. We decompose the scrambled sequence into three sequences of different lengths: S1, S2 and S3. Then, the initial values of the chaotic system are updated by the sequences S2 and S3 and using the updated initial value iterates the chaotic system and generates the key sequence K3, and the sequence S1 is encrypted by the sequence K3 to obtain the cipher sequence C1, using the sequences C1 and S3 updates the initial value of the chaotic system, and using the updated initial value iterates the chaotic system and generates the key sequence K4, and using the sequence K4 encrypts the sequence S2 to obtain the cipher sequence C2. Thus, ciphertext participates in the generation processes of chaotic pseudo-random sequences and key sequences, and the entire encryption process has a closed-loop structure. The experimental results show that the encryption algorithm has high security and sensitivity.

KRAS Promoter G-Quadruplexes from Sequences of Different Length: A Physicochemical Study.

DNA G-quadruplexes (G4s) form in relevant genomic regions and intervene in several biological processes, including the modulation of oncogenes expression, and are potential anticancer drug targets. The human KRAS proto-oncogene promoter region contains guanine-rich sequences able to fold into G4 structures. Here, by using circular dichroism and differential scanning calorimetry as complementary physicochemical methodologies, we compared the thermodynamic stability of the G4s formed by a shorter and a longer version of the KRAS promoter sequence, namely 5′-AGGGCGGTGTGGGAATAGGGAA-3′ (KRAS 22RT) and 5′-AGGGCGGTGTGGGAAGAGGGAAGAGGGGGAGG-3′ (KRAS 32R). Our results show that the unfolding mechanism of KRAS 32R is more complex than that of KRAS 22RT. The different thermodynamic stability is discussed based on the recently determined NMR structures. The binding properties of TMPyP4 and BRACO-19, two well-known G4-targeting anticancer compounds, to the KRAS G4s were also investigated. The present physicochemical study aims to help in choosing the best G4 target for potential anticancer drugs.

Chaos game representation and its applications in bioinformatics.

Chaos game representation (CGR), a milestone in graphical bioinformatics, has become a powerful tool regarding alignment-free sequence comparison and feature encoding for machine learning. The algorithm maps a sequence to 2-dimensional space, while an extension of the CGR, the so-called frequency matrix representation (FCGR), transforms sequences of different lengths into equal-sized images or matrices. The CGR is a generalized Markov chain and includes various properties, which allow a unique representation of a sequence. Therefore, it has a broad spectrum of applications in bioinformatics, such as sequence comparison and phylogenetic analysis and as an encoding of sequences for machine learning. This review introduces the construction of CGRs and FCGRs, their applications on DNA and proteins, and gives an overview of recent applications and progress in bioinformatics.

Detection of Copy Number of Mitochondrial DNA in Iraq Population

Mitochondrial DNA copy number (mtDNA-CN), a measure of the number of mitochondrial genomes percell. A cell contains two copies of genomic DNA compared to a single copy of DNA per chromosome. Thequantitative real-time PCR (qPCR) is the gold standard method for measuring mtDNA-CN, the quantificationafter DNA extraction it is an important step, the quantitation of DNA plays a central role inmedical andforensic DNA analysis, which provides the information about the amount of DNA present in unknownsamples. In this study the quantitative results showed that the higher value 74941 and the lower value 190.A multiplex quantitative PCR assay developed to amplify target sequences of different length, which allowsfor the assessment of DNA degradation in samples of forensic interest. The quantification of DNA by qPCRrelies on the detection of amplified product (amplicon) at each cycle of the PCR.

Comparison of chain microstructure between two propylene−ethylene copolymer resins with bimodal melting temperature distribution

Polypropylene (PP) has been commonly used as packaging materials due to the favorable mechanical and optical properties related to its diverse and complex chain microstructure which can be better understood through effective fractionation. In this work, two commercial PP resins (A and B) with similar bimodal melting temperature distribution in the range of 100–160 °C but different ethylene content (9.1–12.3 mol%) are fractionated by preparative temperature rising elution fractionation (P-TREF) and relevant fractions are characterized by a variety of techniques such as high temperature-gel permeation chromatography (HT-GPC), differential scanning calorimetry (DSC), 13C nuclear magnetic resonance (NMR) spectroscopy, Fourier transform-infrared (FT-IR) spectroscopy, and wide-angle X-ray diffraction (WAXD). The chain microstructures of the two resins used as food packaging film exhibit heterogeneity and some differences, which are mainly caused by broad and different distribution of ethylene comonomers. Resin B contains more ethylene units than resin A, and the ethylene units are incorporated into the PP chains in the forms of individual unit or very short segments, resulting in large amount of fractions with relatively low isotacticity and crystallinity that eluted at the temperature range of 35–90 °C. These fractions account for 84.07 wt% of resin B, whereas 59.66 wt% of resin A. The propylene sequences of different isotacticity and crystallinity generated by the insertion of ethylene comonomers in resin B are mainly responsible for its biomodal melting behavior. However, ethylene comonomers tend to insert into the PP chains of resin A in the forms of long polyethylene (PE) blocks and generate propylene−ethylene block copolymers with high crystallizability which can be eluted at high temperatures of 120–140 °C. These fractions constitute 3.16 wt% of resin A, but barely exist in resin B. Moreover, 35.83 wt% of resin A were eluted at 110 °C, whereas only 13.22 wt% of resin B can be eluted at this temperature. These fractions with high isotacticity and molecular weight eluted at high temperatures contribute to the higher flexural modulus of resin A. Both long ethylene blocks and propylene sequences of different lengths contribute to the biomodal melting peaks on the thermogram of resin A.

Pseudo Random Binary Sequence Based on Cyclic Difference Set

With the increasing reliance on technology, it has become crucial to secure every aspect of online information where pseudo random binary sequences (PRBS) can play an important role in today’s world of Internet. PRBS work in the fundamental mathematics behind the security of different protocols and cryptographic applications. This paper proposes a new PRBS namely MK (Mamun, Kumu) sequence for security applications. Proposed sequence is generated by primitive polynomial, cyclic difference set in elements of the field and binarized by quadratic residue (QR) and quadratic nonresidue (QNR). Introduction of cyclic difference set makes a special contribution to randomness of proposed sequence while QR/QNR-based binarization ensures uniformity of zeros and ones in sequence. Besides, proposed sequence has maximum cycle length and high linear complexity which are required properties for sequences to be used in security applications. Several experiments are conducted to verify randomness and results are presented in support of robustness of the proposed MK sequence. The randomness of proposed sequence is evaluated by popular statistical test suite, i.e., NIST STS 800-22 package. The test results confirmed that the proposed sequence is not affected by approximations of any kind and successfully passed all statistical tests defined in NIST STS 800-22 suite. Finally, the efficiency of proposed MK sequence is verified by comparing with some popular sequences in terms of uniformity in bit pattern distribution and linear complexity for sequences of different length. The experimental results validate that the proposed sequence has superior cryptographic properties than existing ones.

Scalable data series subsequence matching with ULISSE

Data series similarity search is an important operation, and at the core of several analysis tasks and applications related to data series collections. Despite the fact that data series indexes enable fast similarity search, all existing indexes can only answer queries of a single length (fixed at index construction time), which is a severe limitation. In this work, we propose ULISSE, the first data series index structure designed for answering similarity search queries of variable length (within some range). Our contribution is twofold. First, we introduce a novel representation technique, which effectively and succinctly summarizes multiple sequences of different length. Based on the proposed index, we describe efficient algorithms for approximate and exact similarity search, combining disk-based index visits and in-memory sequential scans. Our approach supports non-Z-normalized and Z-normalized sequences and can be used with no changes with both Euclidean distance and dynamic time warping, for answering both k-NN and $$\epsilon $$ -range queries. We experimentally evaluate our approach using several synthetic and real datasets. The results show that ULISSE is several times, and up to orders of magnitude more efficient in terms of both space and time cost, when compared to competing approaches.

HMMs based masquerade detection for network security on with parallel computing

Masquerade detection is currently an active research topic in the field of network security. This paper presents a novel method for detecting masquerade attacks based on hidden Markov models (HMMs), which applies to host-based intrusion detection systems using Unix or Linux shell commands as audit data. The method employs multiple command sequences of different lengths to represent the behavioral patterns of a legitimate user and constructs a specific HMM to characterize the normal behavior profile of the user. Moreover, the adaptability and precision of user profiling are definitely taken into account. During training, the parameters of the HMM are calculated by parallel computing that is less computationally expensive than the classic Baum–Welch algorithm. At the detection stage, the occurrence probabilities of short state sequences are first computed to analyze behavior deviations that may indicate masquerade attacks, and two alternative decision schemes can be used to classify the monitored user’s behavior as normal or anomalous. The method addresses both detection accuracy and computational efficiency and is especially suitable for online detection. Our study empirically demonstrates the promising performance of the method.

Polytypism and Polysomatism in Mixed-Layer Chalcogenides: Characterization of PbBi4Te4S3 and Inferences for Ordered Phases in the Aleksite Series

Bi-Pb-chalcogenides of the aleksite series represent homologous mixed-layer compounds derived from tetradymite (Bi2Te2S). Considering tetradymite as composed of five-atom (Bi2Te2S) layers, the named minerals of the aleksite homologous series, aleksite (PbBi2Te2S2) and saddlebackite, (Pb2Bi2Te2S3) have been considered as phases formed by regular stacking of longer seven- and nine-atom layers. High-angle annular dark-field scanning transmission electron microscope (HAADF-STEM) imaging of thinned foils prepared in-situ on domains deemed homogeneous from electron probe microanalysis, STEM energy-dispersive X-ray spectrometry (EDS) element mapping and fast Fourier transforms (FFTs) from the images offer new insights into these structures. The hitherto-unnamed phase, PbBi4Te4S3, previously interpreted as regular intergrowths of five- and seven-atom layers, is characterized in terms of regular repeats of five- and seven-atom layers over distances of at least 350 nm, defining the (57), or 24H polytype. Imaging of other domains in the same lamella with identical composition at the electron microprobe scale reveals the presence of two additional polytypes: (5559), or 48H; and (557.559) or 72H. Unit cell dimensions for all three polytypes of PbBi4Te4S3 can be both measured and predicted from the HAADF STEM imaging and FFTs. STEM EDS mapping of each polytype confirm the internal structure of each layer. Lead and S occur within the centre of the layers, i.e., Te–Bi–S–Pb–S–Bi–Te in the seven-atom layer, Te–Bi–S–Pb–S–Pb–S–Bi–Te in the nine-atom layer, and so on. Polytypism is an intrinsic feature of the aleksite series, with each named mineral or unnamed phase, except the simple five-atom layer, defined by several alternative stacking sequences of different length, readily explaining the differing c values given in previous work. Homology is defined in terms of layer width and the stacking arrangement of those layers. Coherent structures of the same composition need not only be built of layers of adjacent size (i.e., five- and seven-atom layers) but, as exemplified by the (5559) polytype, may also contain non-adjacent layers, a finding not anticipated in previous work. New polysomes remain to be discovered in nature and each potentially exists as multiple polytypes. The present study further emphasizes the utility of HAADF STEM imaging and atomic-scale STEM EDS element mapping as an optimal tool for tracking stacking sequences and characterising structures in mixed-layer compounds.

Glance and Stare: Trapping Flying Birds in Aerial Videos by Adaptive Deep Spatio-Temporal Features

Flying bird detection has recently attracted increasing attention in computer vision, which becomes an urgent task with the opening up of the low-altitude airspace. However, compared to conventional object detection tasks, it is much more challenging to trap flying birds in aerial videos due to small target sizes, complex backgrounds of great variations and disturbances of bird-like objects. In this paper, we propose a unified framework termed glance-and-stare detection (GSD) to trap flying birds in aerial videos. The GSD is inspired by the fact that human beings first glance at the whole image and then stare at the areas where the suspected object is most likely to appear until the confirmation is obtained. Specifically, we propose the zooming-in algorithm to generate region proposals for accurate localization of flying birds; to represent region proposal sequences of different lengths, we propose adaptive deep spatio-temporal features by leveraging the strength of 3D convolutional neural networks, based on which classification is conducted to achieve final detection. In contrast to conventional methods, the GSD enables localization and classification to be conducted jointly in an alternating iterative way, which mutually enhances each other to improve their performance. In order to validate the proposed GSD algorithm, we build flying bird data sets including images and videos, which provide new benchmarks for evaluation of flying bird detection systems. Experiments on the data sets demonstrate that the GSD can achieve high detection accuracy and largely outperform the state-of-the-art detection methods.

PPR-Meta: a tool for identifying phages and plasmids from metagenomic fragments using deep learning.

BackgroundPhages and plasmids are the major components of mobile genetic elements, and fragments from such elements generally co-exist with chromosome-derived fragments in sequenced metagenomic data. However, there is a lack of efficient methods that can simultaneously identify phages and plasmids in metagenomic data, and the existing tools identifying either phages or plasmids have not yet presented satisfactory performance.FindingsWe present PPR-Meta, a 3-class classifier that allows simultaneous identification of both phage and plasmid fragments from metagenomic assemblies. PPR-Meta consists of several modules for predicting sequences of different lengths. Using deep learning, a novel network architecture, referred to as the Bi-path Convolutional Neural Network, is designed to improve the performance for short fragments. PPR-Meta demonstrates much better performance than currently available similar tools individually for phage or plasmid identification, while testing on both artificial contigs and real metagenomic data. PPR-Meta is freely available via http://cqb.pku.edu.cn/ZhuLab/PPR_Meta or https://github.com/zhenchengfang/PPR-Meta.ConclusionsTo the best of our knowledge, PPR-Meta is the first tool that can simultaneously identify phage and plasmid fragments efficiently and reliably. The software is optimized and can be easily run on a local PC by non-computer professionals. We developed PPR-Meta to promote the research on mobile genetic elements and horizontal gene transfer.

Validation of use of the miniPCR thermocycler for Ebola and Zika virus detection.

The development of point-of-care (POC) diagnostic systems has received well-deserved attention in recent years in the scientific literature, and many experimental systems show great promise in real settings. However, in the case of an epidemic emergency (or a natural disaster), the first line of response should be based on commercially available and validated resources. Here, we compare the performance and ease of use of the miniPCR, a recently commercially available compact and portable PCR device, and a conventional thermocycler for the diagnostics of viral nucleic acids. We used both thermocyclers to detect and amplify Ebola and Zika DNA sequences of different lengths (in the range of 91 to 300 nucleotides) at different concentrations (in the range of ~50 to 4.0 x 108 DNA copies). Our results suggest that the performance of both thermocyclers is quite similar. Moreover, the portability, ease of use, and reproducibility of the miniPCR makes it a reliable alternative for point-of-care nucleic acid detection and amplification.