Custom Sequence Research Articles

SeqLengthPlot v2.0: An All-in-one, Easy-to-Use Tool for Visualizing and Retrieving Sequence Lengths from FASTA Files

Abstract Motivation Accurate sequence length profiling is essential in bioinformatics, particularly in genomics and proteomics. Existing tools like SeqKit and the Trinity toolkit provide basic sequence statistics but often fall short in offering comprehensive analytics and plotting options. For instance, SeqKit is a very complete and fast tool for sequence analysis, delivering useful metrics (e.g., number of sequences, average, minimum, and maximum lengths) and can return sequences either shorter or longer (but not both at once) for a given length. Similarly, Trinity's Perl-based scripts provide detailed contig length distributions (e.g., N50, median, and average lengths) but do not include the total number of sequences or offer graphical representations of the data. Results Given that key sequence analysis tasks are often distributed across multiple tools, we introduce SeqLengthPlot v2.0, an all-in-one, easy-to-use Python-based tool. Through a simple command-line interface, this straightforward tool enables users to split input FASTA files (nucleotide and protein) into two distinct files based on a customizable sequence length cutoff. It also automatically retrieves the resulting FASTA files, generates length distribution plots, and provides comprehensive statistical summaries. Availability and implementation SeqLengthPlot_v2.0.2 can be accessed at https://github.com/danydguezperez/SeqLengthPlot/releases/tag/v2.0.2.

EPCO-22. DEEP PHENOTYPING IDENTIFIES TRUNCATING MUTATIONS IN THE NF2 LINKER DOMAIN ASSOCIATED WITH DISEASE SEVERITY IN NEUROFIBROMATOSIS TYPE II

Abstract INTRODUCTION Neurofibromatosis type 2 (NF2) is an autosomal dominant tumor syndrome with protean presentation and variable penetrance. Our objective was to perform deep phenotypic and genetic analysis of a diverse NF2 population to identify predictors of worse clinical outcome. METHODS We evaluated 167 patients with NF2 and followed them for a mean duration of 4.5 years. Detailed clinical and radiographic characteristics were collected on each patient, and Karnofsky performance scale (KPS) scores and within-study mortality were monitored. CLIA certified genotyping was supplemented with a custom sequencing panel targeting NF2 and other schwannomatosis-related genes. Outcome predictors were assessed using multivariable linear or logistic regression. RESULTS Germline mutations (nonsense 36%, splice-site 16%, and large deletions 16%) were grouped by predicted protein effect (truncating 41%, non-truncating 27%, and mosaic 33%) and mapped to the NF2chromosomal locus. Patients with truncating NF2 mutations had lower mean age of diagnosis (19.9 versus 25.7, P = 0.01) and faster tumor growth rates (2.4% versus 1.1% for all tumors, P = 0.01). Patients whose truncating mutations occurred within the NF2 linker domain (exons 8, 11 and 12) had more total tumors (adjusted coefficient 15.0, 95% CI 0.8 – 29.0, P = 0.04), and specifically more schwannomas (adjusted coefficient 7.0, 95% CI 0.2 – 13.9, P = 0.04). Presence of a-helical domain mutations predicted higher within-study mortality (adjusted OR 4.3, 95% CI 1.1 – 16.2, P = 0.03). CONCLUSIONS The NF2 linker domain functions a hinge, facilitating binding between the tail domain and the LATS binding site within the FERM domain. In our study, truncating mutations in the NF2 linker domain were associated with higher tumor burden and over four-fold within-study mortality. The linker domain may constitute a novel therapeutic target in patients with NF2.

Epigenetic memory of temperature sensed during somatic embryo maturation in 2-year-old maritime pine trees.

Embryogenesis is a brief but potentially critical phase in the tree life cycle for adaptive phenotypic plasticity. Using somatic embryogenesis in maritime pine (Pinus pinaster Ait.), we found that temperature during the maturation phase affects embryo development and post-embryonic tree growth for up to three years. We examined whether this somatic stress memory could stem from temperature- and/or development-induced changes in DNA methylation. For this, we developed a 200 Mb custom sequence capture bisulfite analysis of genes and promoters to identify differentially methylated cytosines (DMCs) between temperature treatments (18, 23, and 28°C) and developmental stages (immature and cotyledonary embryos, shoot apical meristem of 2-year-old plants) and investigate if these differences can be mitotically transmitted from embryonic to post-embryonic development (epigenetic memory). We revealed a high prevalence of temperature-induced DMCs in genes (8-14%) compared to promoters (less than 1%) in all 3 cytosine contexts. Developmental DMCs showed a comparable pattern but only in the CG context and with a strong trend towards hypomethylation, particularly in the promoters. A high percentage of DMCs induced by developmental transitions were found memorized in genes (up to 45-50%) and promoters (up to 90%). In contrast, temperature-induced memory was lower and confined to genes after both embryonic (up to 14%) and post-embryonic development (up to 8%). Using stringent criteria, we identified ten genes involved in defense responses and adaptation, embryo development, and chromatin regulation that are candidates for the establishment of a persistent epigenetic memory of temperature sensed during embryo maturation in maritime pine. Here, we provide evidence that DNA methylation marks established during the embryonic phase are transmitted to the post-embryonic plant development phase.

Reinterpretation of genetic testing for Cardiomyopathies: Less is more

Abstract Background In the last years, the concept of reinterpretation of genetic testing has been considered as part of the diagnostic workflow. However, this process requires time, expert curators and new pieces of evidence to evaluate, as functional studies, segregation and allele frequency data. Last year, the European Society of Cardiology (ESC) published new guidelines for the management of cardiomyopathies. This document includes a precise list of genes associated with the different types of Cardiomyopathies and their level of contribution, according to ClinGen evaluation: definitive/strong, moderate, limited, and refuted/disputed. This document poses a real support in the reinterpretation of tests that report variants in genes not clearly correlated with these phenotypes. Purpose To evaluate the impact of the new ESC indications regarding genetic testing, we reconsidered all genetic results performed in our Clinic in patients with confirmed or suspicious Cardiomyopathy. Methods From 2017 to 2023 we analyzed 296 probands eligible for Cardiomyopathy genetic testing, in particular: 105 patients were tested for Arrhythmogenic Cardiomyopathy (ACM), 113 for Hypertrophic Cardiomyopathy (HCM) and 78 for Dilated Cardiomyopathy (DCM), using a custom sequencing panel including 44 genes (11 genes for ACM, 34 for DCM and 29 for HCM). Results Comparison analysis with the genes reported from the ESC guidelines, considering only the definitive, moderate, and strong genes, showed that 24 genes are not more associated with the phenotype and then not clinically actionable. In this scenario, we reconsidered 12 variants (8%), previously classified as VUS, changing the referral status of 8 patients, from uncertain to negative. Conclusions These results highlight the importance of variant reinterpretation as a fundamental step of genetic testing, posing the importance of the selection of the genes to analyze, in order to improve the management of the patients.

Discovering Novel Proteoforms Using Proteogenomic Workflows Within the Galaxy Bioinformatics Platform.

Proteogenomics is a growing "multi-omics" research area that combines mass spectrometry-based proteomics and high-throughput nucleotide sequencing technologies. Proteogenomics has helped in genomic annotation for organisms whose complete genome sequences became available by using high-throughput DNA sequencing technologies. Apart from genome annotation, this multi-omics approach has also helped researchers confirm expression of variant proteins belonging to unique proteoforms that could have resulted from single-nucleotide polymorphism (SNP), insertion and deletions (Indels), splice isoforms, or other genome or transcriptome variations.A proteogenomic study depends on a multistep informatics workflow, requiring different software at each step. These integrated steps include creating an appropriate protein sequence database, matching spectral data against these sequences, and finally identifying peptide sequences corresponding to novel proteoforms followed by variant classification and functional analysis. The disparate software required for a proteogenomic study is difficult for most researchers to access and use, especially those lacking computational expertise. Furthermore, using them disjointedly can be error-prone as it requires setting up individual parameters for each software. Consequently, reproducibility suffers. Managing output files from each software is an additional challenge. One solution for these challenges in proteogenomics is the open-source Web-based computational platform Galaxy. Its capability to create and manage workflows comprised of disparate software while recording and saving all important parameters promotes both usability and reproducibility. Here, we describe a workflow that can perform proteogenomic analysis on a Galaxy-based platform. This Galaxy workflow facilitates matching of spectral data with a customized protein sequence database, identifying novel protein variants, assessing quality of results, and classifying variants along with visualization against the genome.

Molecular identification and genetic variability of equine and bovine ocular setariasis in india: molecular profiling by mitochondrial genes.

Ocular setariasis is an ectopic infection caused by a parasite under the genus Setaria. Adult worms belong to the Setariidae family and typically reside in the peritoneal cavity of ungulates. However, immature forms of these species may aberrantly migrate to the eyes of cattle, buffalo, goats, horses and several other hosts, leading to corneal opacity and blindness. Here, we have distinguished the Setaria digitata collected from both equine and buffalo hosts based on the morphology, molecular profiling of mitochondrial cytochrome c oxidase subunit 1 (Cox1), cytochrome c oxidase subunit 3 (Cox3) and, Nicotinamide Adenine Dinucleotide dehydrogenase subunit 1 (NAD1) genes. A single filarial worm was collected from the eye of one equine and one bovine host. These worms were then processed for morphological examination and DNA isolation. Cox1, Cox3 and NAD1 genes were amplified using specific primers and subjected to custom sequencing. The sequences were then used for multiple sequence alignment, assessment of entropy, similarity and haplotype diversity analysis. Key morphological features confirmed the worms collected were male and female Setaria digitata from equine and buffalo hosts, respectively. Cox1, Cox3 and NAD1 gene sequence analysis showed a close association of S.digitata Indian isolates with its counterparts from Sri Lanka and China isolates. The phylogram of bovine S. digitata sequences shows a close relationship to other equine S. digitata sequences, indicating the need for further in-depth studies on the prevalence of infection across various host species and intermediate hosts. Although the sequence results suggest that S. digitata is likely the causative agent of ocular setariasis in India, additional samples are needed to confirm this conclusion. Comprehensive analysis of the transcriptome and proteome of S. digitata from both bovine and equine hosts is necessary to explore variations in host-parasite interactions. These findings will aid in future parasite identification, investigations into vector prevalence in India, and the development of control measures against ocular setariasis.

IsolateR: an R package for generating microbial libraries from Sanger sequencing data.

Sanger sequencing of taxonomic marker genes (e.g. 16S/18S/ITS/rpoB/cpn60) represents the leading method for identifying a wide range of microorganisms including bacteria, archaea, and fungi. However, the manual processing of sequence data and limitations associated with conventional BLAST searches impede the efficient generation of strain libraries essential for cataloging microbial diversity and discovering novel species. isolateR addresses these challenges by implementing a standardized and scalable three-step pipeline that includes: (1) automated batch processing of Sanger sequence files, (2) taxonomic classification via global alignment to type strain databases in accordance with the latest international nomenclature standards, and (3) straightforward creation of strain libraries and handling of clonal isolates, with the ability to set customizable sequence dereplication thresholds and combine data from multiple sequencing runs into a single library. The tool's user-friendly design also features interactive HTML outputs that simplify data exploration and analysis. Additionally, in silico benchmarking done on two comprehensive human gut genome catalogues (IMGG and Hadza hunter-gather populations) showcase the proficiency of isolateR in uncovering and cataloging the nuanced spectrum of microbial diversity, advocating for a more targeted and granular exploration within individual hosts to achieve the highest strain-level resolution possible when generating culture collections. isolateR is available at: https://github.com/bdaisley/isolateR.

CRIMP: a CRISPR/Cas9 insertional mutagenesis protocol and toolkit

Site-directed insertion is a powerful approach for generating mutant alleles, but low efficiency and the need for customisation for each target has limited its application. To overcome this, we developed a highly efficient targeted insertional mutagenesis system, CRIMP, and an associated plasmid toolkit, CRIMPkit, that disrupts native gene expression by inducing complete transcriptional termination, generating null mutant alleles without inducing genetic compensation. The protocol results in a high frequency of integration events and can generate very early targeted insertions, during the first cell division, producing embryos with expression in one or both halves of the body plan. Fluorescent readout of integration events facilitates selection of successfully mutagenized fish and, subsequently, visual identification of heterozygous and mutant animals. Together, these advances greatly improve the efficacy of generating and studying mutant lines. The CRIMPkit contains 24 ready-to-use plasmid vectors to allow easy and complete mutagenesis of any gene in any reading frame without requiring custom sequences, modification, or subcloning.

An Iterated Local Search Heuristic for the Multi-Trip Vehicle Routing Problem with Multiple Time Windows

This paper studies the multi-trip vehicle routing problem with multiple time windows, which extends the multi-trip vehicle routing problem by deciding not only the sequence of customers that each vehicle serves but also the service time window of each customer. It also requires that the delivery service time is within the selected time windows and that the total demand of the customers served by the vehicle on each trip does not exceed the maximum carrying capacity. For solving the studied problem, we develop a mixed integer linear programming model with the objective of minimizing the total travel distance of vehicles and design a tailored iterative local search heuristic. Within the framework of the iterative local search, an improved Solomon greedy insertion algorithm suitable for multiple time windows and multi-trip scenarios is designed to generate the initial solution, and local search operators such as Or-opt and Relocate, as well as Random Exchange perturbation operations, are also developed. The experiment results demonstrate the effectiveness of the proposed model and algorithm and confirm that by providing customers with multiple time windows option, carriers can flexibly plan vehicle routes and select appropriate service time windows, thereby reducing the number of vehicles used and the total distance travelled and improve delivery success.

A competitive heuristic algorithm for vehicle routing problems with drones

We propose a heuristic algorithm capable of handling multiple variants of the vehicle routing problem with drones (VRPD). Assuming that the drone may be launched from a node and recovered at another, these variants are characterized by three axes, (1) minimizing the transportation cost or minimizing the makespan, (2) the drone is either allowed or not allowed to land while awaiting recovery, and (3) single or multiple trucks each equipped with a drone. In our algorithm, we represent a VRPD solution as a set of customer sequences and evaluate it via local search procedures solving for each sequence a problem that we refer to as the fixed route drone dispatch problem (FRDDP). Given a sequence of customers to be served by a single truck and its drone, the FRDDP selects a subset of customers to be served by the drone and determines drone launch and recovery nodes, while ensuring that each such customer is positioned between two nodes in the initial sequence. We introduce a heuristic dynamic program (HDP) to solve the FRDDP with reduced computational complexity compared to an exact solution algorithm for the problem. We reinforce our algorithm by developing filtering strategies based on the HDP. We benchmark the performance of our algorithm on nine benchmark sets pertaining to four VRPD variants resulting in 932 instances. Our algorithm computes 651 of 680 optimal solutions and identifies 189 new best-known solutions.

Purification of DNA Nanoparticles Using Photocleavable Biotin Tethers.

The number of applications of self-assembled deoxyribonucleic acid (DNA) origami nanoparticles (DNA NPs) has increased drastically, following the development of a variety of single-stranded template DNA (ssDNA) that can serve as the scaffold strand. In addition to viral genomes, such as M13 bacteriophage and lambda DNAs, enzymatically produced ssDNA from various template sources is rapidly gaining traction and being applied as the scaffold for DNA NP preparation. However, separating fully formed DNA NPs that have custom scaffolds from crude assembly mixes is often a multistep process of first separating the ssDNA scaffold from its enzymatic amplification process and then isolating the assembled DNA NPs from excess precursor strands. Only then is the DNA NP sample ready for downstream characterization and application. In this work, we highlight a single-step purification of custom sequence- or M13-derived scaffold-based DNA NPs using photocleavable biotin tethers. The process only requires an inexpensive ultraviolet (UV) lamp, and DNA NPs with up to 90% yield and high purity are obtained. We show the versatility of the process in separating two multihelix bundle structures and a wireframe polyhedral architecture.

The astounding exhaustiveness and speed of the Astral mass analyzer for highly complex samples is a quantum leap in the functional analysis of microbiomes

BackgroundBy analyzing the proteins which are the workhorses of biological systems, metaproteomics allows us to list the taxa present in any microbiota, monitor their relative biomass, and characterize the functioning of complex biological systems.ResultsHere, we present a new strategy for rapidly determining the microbial community structure of a given sample and designing a customized protein sequence database to optimally exploit extensive tandem mass spectrometry data. This approach leverages the capabilities of the first generation of Quadrupole Orbitrap mass spectrometer incorporating an asymmetric track lossless (Astral) analyzer, offering rapid MS/MS scan speed and sensitivity. We took advantage of data-dependent acquisition and data-independent acquisition strategies using a peptide extract from a human fecal sample spiked with precise amounts of peptides from two reference bacteria.ConclusionsOur approach, which combines both acquisition methods, proves to be time-efficient while processing extensive generic databases and massive datasets, achieving a coverage of more than 122,000 unique peptides and 38,000 protein groups within a 30-min DIA run. This marks a significant departure from current state-of-the-art metaproteomics methodologies, resulting in broader coverage of the metabolic pathways governing the biological system. In combination, our strategy and the Astral mass analyzer represent a quantum leap in the functional analysis of microbiomes.DtQfodvXmxPWcwvjCbxEj1Video

Molecular Dynamics and In Vitro Studies Elucidating the Tunable Features of Reconfigurable Nanodiscs for Guiding the Optimal Design of Curcumin Formulation.

In this study, we advance our exploration of Apolipoprotein A-I (apoA-I) peptide analogs (APAs) for their application in nanodisc (ND) assembly, focusing on the dynamic conformational characteristics and the potential for drug delivery. We explore APA-ND interactions with an emphasis on curcumin encapsulation, utilizing molecular dynamic simulations and in vitro assessments to evaluate the efficacy of various APA-ND formulations as drug carriers. The methodological approach involved the generation of three unique apoA-I α-11/3 helical mimics, resulting in fifteen distinct APAs. Their structural integrity was rigorously assessed using ColabFold-AF2, with particular attention to pLDDT and pTM scores. Extensive molecular dynamics simulations, covering 1.7 μs across 17 ND systems, were conducted to investigate the influence of APA sequence variations on ND stability and interactions. This study reveals that the composition of APAs, notably the presence of Proline, Serine, and Tryptophan, significantly impacts ND stability and morphology. Oligomeric APAs, in particular, demonstrated superior stability and distinct interaction patterns compared to their monomeric counterparts. Additionally, hydrodynamic diameter measurements over eight weeks indicated sequence-dependent stability, highlighting the potential of specific APA configurations for sustained colloidal stability. In vitro study successfully encapsulated curcumin in [AA]3/DMPC ND formulations, revealing concentration-dependent stability and interaction dynamics. The findings underscore the remarkable capability of APA-NDs to maintain structural integrity and efficient drug encapsulation, positioning them as a promising platform for drug delivery. The study concludes by emphasizing the tunability and versatility of APA-NDs in drug formulation, potentially revolutionizing nanomedicine by enabling customized APA sequences and ND properties for targeted drug delivery.

Shear shock wave injury in vivo: High frame-rate ultrasound observation and histological assessment

Using high frame-rate ultrasound and ¡1μm sensitive motion tracking we previously showed that shear waves at the surface of ex vivo and in situ brains develop into shear shock waves deep inside the brain, with destructive local accelerations. However post-mortem tissue cannot develop injuries and has different viscoelastodynamic behavior from in vivo tissue. Here we present the ultrasonic measurement of the high-rate shear shock biomechanics in the in vivo porcine brain, and histological assessment of the resulting axonal pathology. A new biomechanical model of brain injury was developed consisting of a perforated mylar surface attached to the brain and vibrated using an electromechanical shaker. Using a custom sequence with 8 interleaved wide beam emissions, brain imaging and motion tracking were performed at 2900 images/s. Shear shock waves were observed for the first time in vivo wherein the shock acceleration was measured to be 2.6 times larger than the surface acceleration ( 95g vs. 36g). Histopathology showed axonal damage in the impacted side of the brain from the brain surface, accompanied by a local shock-front acceleration of >70g. This shows that axonal injury occurs deep in the brain even though the shear excitation was at the brain surface, and the acceleration measurements support the hypothesis that shear shock waves are responsible for deep traumatic brain injuries.

It’s Not Always about Wide and Deep Models: Click-Through Rate Prediction with a Customer Behavior-Embedding Representation

Alongside natural language processing and computer vision, large learning models have found their way into e-commerce. Especially, for recommender systems and click-through rate prediction, these models have shown great predictive power. In this work, we aim to predict the probability that a customer will click on a given recommendation, given only its current session. Therefore, we propose a two-stage approach consisting of a customer behavior-embedding representation and a recurrent neural network. In the first stage, we train a self-supervised skip-gram embedding on customer activity data. The resulting embedding representation is used in the second stage to encode the customer sequences which are then used as input to the learning model. Our proposed approach diverges from the prevailing trend of utilizing extensive end-to-end models for click-through rate prediction. The experiments, which incorporate a real-world industrial use case and a widely used as well as openly available benchmark dataset, demonstrate that our approach outperforms the current state-of-the-art models. Our approach predicts customers’ click intention with an average F1 accuracy of 94% for the industrial use case which is one percentage point higher than the state-of-the-art baseline and an average F1 accuracy of 79% for the benchmark dataset, which outperforms the best tested state-of-the-art baseline by more than seven percentage points. The results show that, contrary to current trends in that field, large end-to-end models are not always needed. The analysis of our experiments suggests that the reason for the performance of our approach is the self-supervised pre-trained embedding of customer behavior that we use as the customer representation.

Quantifying orthogonal barcodes for sequence census assays.

Barcode-based sequence census assays utilize custom or random oligonucloetide sequences to label various biological features, such as cell-surface proteins or CRISPR perturbations. These assays all rely on barcode quantification, a task that is complicated by barcode design and technical noise. We introduce a modular approach to quantifying barcodes that achieves speed and memory improvements over existing tools. We also introduce a set of quality control metrics, and accompanying tool, for validating barcode designs. https://github.com/pachterlab/kb_python, https://github.com/pachterlab/qcbc.

CASBA neurological assessment: Noninvasive EEG biomarkers classify amyloid‐PET, clinical diagnosis, cognitive dementia rating, and FCSRT free recall scores in patients with cognitive decline

AbstractBackgroundAlzheimer’s Disease (AD) is difficult to diagnose and stage, often relying on burdensome clinical tests for neural protein aggregation and cognitive performance. Delays and errors in the accurate diagnosis of AD reduce patient outcomes and prevent early therapeutic intervention. The CASBA electroencephalography (EEG) system leverages a custom sequence of dynamic visual stimuli to elicit and translate novel and noninvasive neural signals biomarkers into accurate estimates of amyloid burden, cognitive performance, and clinical diagnosis, to facilitate clinical decision‐making in patients with cognitive decline.MethodIn a board‐approved study, human patients with mild cognitive impairment (MCI; 71.2 ± 8.2 years old), subjective cognitive decline (SCD; 70.7 ± 6.3 years old), and AD diagnosis (71.2 ± 6.6 years old), alongside amyloid PET (Anti‐Serum Amyloid P Antibody), cognitive testing (i.e. FCSRT), and clinical dementia rating (CDR). Neural signals were collected on a 64‐channel equidistant EEG system (2048Hz, Ag/AgCl; ANT Neuro, Enschede)]. Automated preprocessing and machine learning classification pipelines were developed to extract EEG features and calculate estimates of physiological and cognitive AD metrics from these EEG biomarkers.ResultCASBA accurately classified normal vs. subnormal FCSRT free recall score (threshold of 18, 77.3% balanced accuracy, 84.0% specificity, 70.6% sensitivity), SCD v. MCI/AD clinical diagnosis (79.4% balanced accuracy, 77.3% specificity, 81.5% sensitivity), CDR (threshold of 1, 79.2% balanced accuracy, 76.9% specificity, 81.5% sensitivity), and amyloid‐PET (composite threshold of 1.25, 83.0% balanced accuracy, 75.0% specificity, 91.9% sensitivity).ConclusionThe CASBA neurological assessment accurately predicts important clinical data including patient diagnosis and staging, amyloid burden, and performance on key evaluations of cognition in AD. CASBA supports physician decision making and patient stratification and suggests a new method for noninvasive tracking and repeat assessment during clinical trials. As such, the CASBA neurological assessment offers a promising new tool to advance clinical care and innovation for patients with neurodegenerative disease.

Traffic Flow Forecast using Time Series Analysis based on Machine Learning Algorithm

Time series forecasting plays a critical role in predicting future trends and making informed decisions across various domains. In this research paper, we present a multi-model approach to improve the accuracy and robustness of time series forecasting. Our methodology involves integrating two powerful models, Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA), to capture complex temporal patterns and seasonality in the data. We leverage real-world traffic data from different junctions and employ a comprehensive pipeline for data preprocessing, including data selection, resampling, and sequence generation. Our study focuses on four junctions, and for each junction, we individually train LSTM models, followed by SARIMA models for comparison. The LSTM models are designed to capture long-term dependencies in the time series data, while the SARIMA models are tailored to handle seasonality and autocorrelation. We implement a custom sequence generation process and split the dataset into training and testing sets for model evaluation. The LSTM models are trained using Pytorch, optimizing them for accurate short-term predictions. Our results show that the combination of LSTM and SARIMA models yields superior forecasting performance compared to using each model individually. We present a detailed analysis of the forecasted results, including Root Mean Square Error (RMSE) calculations and visualizations to demonstrate the effectiveness of our multi-model approach. This research contributes to the field of time series forecasting by showcasing the benefits of combining deep learning and classical statistical methods. The proposed approach provides a flexible and robust framework applicable to various time series prediction tasks, offering improved accuracy and reliability in forecasting future trends.

A note on “A unified solution framework for multi-attribute vehicle routing problems”

In this note, the authors propose correcting one erroneous formula from [Vidal, T., Crainic, T. G., Gendreau, M., & Prins, C. (2014). A unified solution framework for multi-attribute vehicle routing problems. European Journal of Operational Research, 234(3), 658–673] in charge of lunch breaks. In the original paper, the authors propose to compute several attribute values from the solution of a vehicle routing problem; like the earliest and latest starting time for sequences of customers to visit. The computed values allow us to quickly evaluate the feasibility and the marginal cost of some neighbor solutions. Several variants of the class of vehicle routing problems can be addressed using this approach. In the case of drivers’ lunch break scheduling, the proposed formula combines optimistic values for the earliest and the latest completion times of the sequence of customers to visit. Using these values to evaluate neighbor solutions, may conclude that unfeasible solutions are feasible, or underestimate the completion time of a driver’s route. In this note, we describe a counter-example to identify the error in the formula. We also adapt the formula to the correct result.

PyBrick-DNA: A Python-Based Environment for Automated Genetic Component Assembly.

Genetic component assembly is key in the simulation and implementation of genetic circuits. Automating this process, thus accelerating prototyping, is a necessity. We present pyBrick-DNA, a software written in Python, that assembles components for the construction of genetic circuits. pyBrick-DNA (colab.pyBrick.com) is a user-friendly environment where scientists can select genetic sequences or input custom sequences to build genetic assemblies. All components are modularly fused to generate a ready-to-go single DNA fragment. It includes Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and plant gene-editing components. Hence, pyBrick-DNA can generate a functional CRISPR construct composed of a single-guided RNA integrated with Cas9, promoters, and terminator elements. The outcome is a DNA sequence, along with a graphical representation, composed of user-selected genetic parts, ready to be synthesized and cloned in vivo. Moreover, the sequence can be exported as a GenBank file allowing its use with other synthetic biology tools.