True Sequence Research Articles

Chlomito: a novel tool for precise elimination of organelle genome contamination from nuclear genome assembly.

Accurate reference genomes are fundamental to understanding biological evolution, biodiversity, hereditary phenomena and diseases. However, many assembled nuclear chromosomes are often contaminated by organelle genomes, which will mislead bioinformatic analysis, and genomic and transcriptomic data interpretation. To address this issue, we developed a tool named Chlomito, aiming at precise identification and elimination of organelle genome contamination from nuclear genome assembly. Compared to conventional approaches, Chlomito utilized new metrics, alignment length coverage ratio (ALCR) and sequencing depth ratio (SDR), thereby effectively distinguishing true organelle genome sequences from those transferred into nuclear genomes via horizontal gene transfer (HGT). The accuracy of Chlomito was tested using sequencing data from Plum, Mango and Arabidopsis. The results confirmed that Chlomito can accurately detect contigs originating from the organelle genomes, and the identified contigs covered most regions of the organelle reference genomes, demonstrating efficiency and precision of Chlomito. Considering user convenience, we further packaged this method into a Docker image, simplified the data processing workflow. Overall, Chlomito provides an efficient, accurate and convenient method for identifying and removing contigs derived from organelle genomes in genomic assembly data, contributing to the improvement of genome assembly quality.

A high-quality chromosome-level genome assembly of the topmouth culter (Culter alburnus Basilewsky, 1855)

Culter alburnus is extensively distributed in various rivers and lakes across China. As a widely adaptive fish species, it has significant economic values and special ecological roles. To meet research demands and provide better genomic resources, in this research, a chromosome-level genome assembly was constructed using HiFi long-reads and Hi-C sequencing data. Compared with the published versions, our genome assembly is of higher quality with only 31 gaps and closer to its true structure and sequence. The genome size was 1.052 Gb, with a contig N50 of 32.92 Mb and a scaffold N50 of 43.09 Mb. 55 contigs were anchored to 24 chromosomes on the basis of Hi-C data. A total of 598.23 Mb of repetitive sequences were annotated and 28,228 protein-coding genes were predicted. Additionally, BUSCO assessment indicated assembly and annotation scores of 98.3% and 99.2%, respectively. This high-quality genome will provide scientific support for excavating the species characteristics of C. alburnus and exploring its molecular mechanisms in response to environmental changes and stress.

Nonlinear Charge Transport and Excitable Phenomena in Semiconductor Superlattices.

Semiconductor superlattices are periodic nanostructures consisting of epitaxially grown quantum wells and barriers. For thick barriers, the quantum wells are weakly coupled and the main transport mechanism is a sequential resonant tunneling of electrons between wells. We review quantum transport in these materials, and the rate equations for electron densities, currents, and the self-consistent electric potential or field. Depending on superlattice configuration, doping density, temperature, voltage bias, and other parameters, superlattices behave as excitable systems, and can respond to abrupt dc bias changes by large transients involving charge density waves before arriving at a stable stationary state. For other parameters, the superlattices may have self-sustained oscillations of the current through them. These oscillations are due to repeated triggering and recycling of charge density waves, and can be periodic in time, quasiperiodic, and chaotic. Modifying the superlattice configuration, it is possible to attain robust chaos due to wave dynamics. External noise of appropriate strength can generate time-periodic current oscillations when the superlattice is in a stable stationary state without noise, which is called the coherence resonance. In turn, these oscillations can resonate with a periodic signal in the presence of sufficient noise, thereby displaying a stochastic resonance. These properties can be exploited to design and build many devices. Here, we describe detectors of weak signals by using coherence and stochastic resonance and fast generators of true random sequences useful for safe communications and storage.

A 0.012‐mm2 0.244‐pJ/bit successive approximation register analog‐to‐digital converter‐based true random number generator for Internet of Things applications in a 65‐nm complementary metal–oxide–semiconductor

SummaryThis paper proposes a true random number generator (TRNG) based on a successive approximation register (SAR) analog‐to‐digital converter (ADC). A top‐plate sampling SAR ADC structure and a new MSB‐isolation switch scheme (MISS) are combined to achieve low‐power operation and small area occupancy at the same time. When the SAR ADC has completed its conversion, a secondary firing of the comparator measures the remaining signal. This 1‐bit quantized residue serves as the true random sequence, thus eliminating the need for extra circuitry in the TRNG. A 65‐nm prototype of the proposed TRNG was found to occupy an area of 0.012 mm2 and successfully passed all the National Institute of Standards and Technology (NIST) tests without post‐processing. It achieved a figure‐of‐merit of 0.244 pJ/bit for a 1 Mb/s random sequence with a 0.8‐V power supply. Its resilience to power noise injection attacks was also verified.

Extant Sequence Reconstruction: The Accuracy of Ancestral Sequence Reconstructions Evaluated by Extant Sequence Cross-Validation

Ancestral sequence reconstruction (ASR) is a phylogenetic method widely used to analyze the properties of ancient biomolecules and to elucidate mechanisms of molecular evolution. Despite its increasingly widespread application, the accuracy of ASR is currently unknown, as it is generally impossible to compare resurrected proteins to the true ancestors. Which evolutionary models are best for ASR? How accurate are the resulting inferences? Here we answer these questions using a cross-validation method to reconstruct each extant sequence in an alignment with ASR methodology, a method we term “extant sequence reconstruction” (ESR). We thus can evaluate the accuracy of ASR methodology by comparing ESR reconstructions to the corresponding known true sequences. We find that a common measure of the quality of a reconstructed sequence, the average probability, is indeed a good estimate of the fraction of correct amino acids when the evolutionary model is accurate or overparameterized. However, the average probability is a poor measure for comparing reconstructions from different models, because, surprisingly, a more accurate phylogenetic model often results in reconstructions with lower probability. While better (more predictive) models may produce reconstructions with lower sequence identity to the true sequences, better models nevertheless produce reconstructions that are more biophysically similar to true ancestors. In addition, we find that a large fraction of sequences sampled from the reconstruction distribution may have fewer errors than the single most probable (SMP) sequence reconstruction, despite the fact that the SMP has the lowest expected error of all possible sequences. Our results emphasize the importance of model selection for ASR and the usefulness of sampling sequence reconstructions for analyzing ancestral protein properties. ESR is a powerful method for validating the evolutionary models used for ASR and can be applied in practice to any phylogenetic analysis of real biological sequences. Most significantly, ESR uses ASR methodology to provide a general method by which the biophysical properties of resurrected proteins can be compared to the properties of the true protein.

LS-14 test suite for long sequences

Random number sequences are used in many branches of science. Because of many techni- cal reasons and their practicality, pseudo random sequences are usually employed in place of true number sequences. Whether a sequence generated through a deterministic process is a pseudo random, in other words, random-looking sequence or it contains certain pat- terns, can be determined with the help of statistics and mathematics. Although, in the literature there are many statistical randomness tests for this purpose, there is no much work on test suites specialized for long sequences, that is sequences of length 1,000,000 bits or more. Most of the randomness tests for long sequences use some mathematical approximations to compute expected values of the random variables and hence their results contain some errors. Another approach to evaluate randomness criteria of long sequences is to partition the long sequence into a collection short sequences and evaluate the collection for the ran- domness using statistical goodness of fit tests. The main advantage of this approach is, as the individual sequences are short, there is no need to use mathematical approximations. On the other hand when the second approach is preferred, partition the long sequence into a collection of fixed length subsequences and this approach causes a loss of information in some cases. Hence the idea of dynamic partition should be included to perform a more reliable test suite. In this paper, we propose three new tests, namely the entire R2 run, dynamic saturation point, and dynamic run tests. Moreover, we introduce a new test suite, called LS-14, consisting of 14 tests to evaluate randomness of long sequences. As LS-14 employs all three approaches: testing the entire long sequence, testing the collection of fixed length partitions of it, and finally, testing the collection obtained by the dynamic partitions of it, the proposed LS-14 test suit differs from all existing suites. Mutual comparisons of all 14 tests in the LS-14 suite, with each other are computed. Moreover, results obtained from the proposed test suite and NIST SP800-22 suite are compared. Examples of sequences with certain patterns which are not observed by NIST SP800-22 suite but detected by the proposed test suite are given.

Optimization-informed Rule Extraction for HVAC system: A Case Study of Dedicated Outdoor Air System Control in a Mixed-Humid Climate Zone.

In the era of post-Coronavirus Disease 2019, the dedicated outdoor air system (DOAS), which provides 100% outdoor air for the building, is widely acknowledged as it can ensure acceptable indoor air quality by delivering fresh outdoor air to occupied space. The DOAS with a proper design and operation can provide sufficient ventilation and dehumidification while achieving energy efficiency. Nonetheless, there is limited guidance in determining the optimal control sequence of the DOAS for the designers and operators to implement in practice. Accordingly, in practice, a number of issues have been acknowledged in the design and control phases of DOAS, including insufficient ventilation and dehumidification, and increasing supply air dry-bulb temperature in fear of over-cooling, which might cause significant discomfort and energy waste. There have been efforts to develop high-performing DOAS controls for better energy efficiency. However, such controls are often complex, or difficult to interpret, for building designers and operators to consider in practice. In this regard, this paper explores a simulation-based framework for generating a supply air temperature control sequence of the DOAS not only to ensure improved energy-saving potential but also to guarantee the implement-ability of the control logic. The U.S Department of Energy prototype primary school with dynamic occupancy profiles was modeled with a whole building simulation program, EnergyPlus. The model consists of a DOAS with an exhaust air energy recovery system for ventilation and fan-coil units for space cooling and heating. Then, a Genetic Algorithm was adopted to find the true optimal supply air temperature control sequence in terms of minimizing the energy cost of the heating, ventilation, and air conditioning system operation. Lastly, Decision Tree was adopted to extract rules out of the optimums to derive an implementable sequence of operation for the DOAS supply air temperature. A total of 12 week-simulation including four weeks of heating, cooling, and shoulder seasons, separately, under the weather condition of New York City was conducted for the case study. This case study identified that the optimization-informed rule extraction-based control, when compared to conventional outdoor air temperature-based reset control, could save about 13% of energy cost and 25% of energy consumption throughout the heating, cooling, and shoulder seasons. It is notable that the energy-saving was mainly achieved by reducing the heating energy consumption. Importantly, it nearly corresponds to the true optimal control result, which reduces approximately 14% of energy cost and 27% of energy consumption. From the results, it can be highlighted that the optimization-informed rule extraction can be as energy effective as the optimal control, while significantly reducing the complexity of the control.

Physically non-cloneable arbiter -type function with non-linear path pairs

Physically unclonable functions (PUFs) are basic physical cryptographical primitives, providing to solve tasks such as unclonable identification, digital device authentication and copyright authentication, true random sequence generation, etc. The major features of PUFs are stability, unpredictability and irreproducibility, due to uncontrollable random variations of distinctive features of the raw materials and technological processes used during their manufacturing. Generally, PUF are digital circuits that extract such variations and convert them into a binary format, which applied for further use. Among the variety of PUF types, an Arbiter PUF (APUF) is distinguished, which is a digital circuit with N-bit challenge input and single output for one-bit response generation. The functionality of APUF is based on comparison of transition time of two copies of the test signal along a pair of configurable paths, selected by the challenge value CH from a set of 2N all possible pairs. The result of the comparison is the binary value of the response. The set of all challenge-response pairs is a random, unpredictable and irreproducible in the cases of implementation of cloned PUF circuits both on single and/or on another chips, also using different technologies. This article presents a new approach to the synthesis of the APUF circuits, based on the permutation network elements, which allow to construct the nonlinear structures of pair of paths. This implies the potential complication of building an APUF model to attack its implemented instances. This article presents new schematic solutions for the synthesis of APUF circuits. Also, the main characteristics of the proposed APUF circuits implemented on the Xilinx Zynq-7000 FPGA is analyzed.

Evaluation of True Random Number Sequences Generated by Utilizing Timing Jitters in Superconducting Integrated Circuits

A Hardware Random Number Generator (HRNG), which generates true random number sequences, is studied. In superconducting Single Flux Quantum (SFQ) circuitry, HRNGs that utilized timing jitters in trigger signals have been demonstrated. In this paper, we propose an HRNG with two oscillators to enhance stability against variation of oscillator frequencies. We investigate the characteristics of random numbers generated with the proposed HRNG not only by numerical simulation but also by experiments using Nb test circuits. In the numerical simulation, the probability of “1” in random number sequences has a local minimum of “1” output probability at conditions where fclk (the clock oscillator frequency) and fgate (the gate oscillator frequency) are approximately equal (fclk ≈ fgate ) for fclk ≤ 60 GHz. It is also found that pass rates for the FIPS140-2 random number tests are little affected by variations of fclk at fgate = 69.2 GHz. The random number generation rate for fclk = 60 GHz and fgate = 43 GHz reaches 3.98 GHz. In experimental measurements, random numbers of 1 Mbits generated by a test circuit pass the SP800-22 random number tests at fclk = 48.79 GHz and fgate = 19.81 GHz.

The effects of stimulating the cerebellum on social sequences: A tDCS-fMRI pilot study: Los efectos de estimular el cerebelo en secuencias sociales: Un estudio piloto con tDCS y fMRI.

Research on the involvement of the cerebellum in social behavior and its relationship with social mentalizing has just begun. Social mentalizing is the ability to attribute mental states such as desires, intentions, and beliefs to others. This ability involves the use of social action sequences which are believed to be stored in the cerebellum. In order to better understand the neurobiology of social mentalizing, we applied cerebellar transcranial direct current stimulation (tDCS) on 23 healthy participants in the MRI scanner, immediately followed by measuring their brain activity during a task that required to generate the correct sequence of social actions involving false (i.e., outdated) and true beliefs, social routines and non-social (control) events. The results revealed that stimulation decreased task performance along with decreased brain activation in mentalizing areas, including the temporoparietal junction and the precuneus. This decrease was strongest for true belief sequences compared to the other sequences. These findings support the functional impact of the cerebellum on the mentalizing network and belief mentalizing, contributing to the understanding of the role of the cerebellum in social sequences.

Graph Theoretic Approach to Randomness Test Based on the Overlapping Blocks

Cryptographic parameters such as secret keys, should be chosen randomly and at the same time it should not be
 so difficult to reproduced them when necessary. Because of this, pseudorandom bit (or number) generators take the role of true random generators. Outputs of pseudorandom generators, although they are produced through some deterministic process, should be random looking, that is not distinguishable from true random sequences. In other word they should not follow any pattern. In this paper we propose a new approach using graph theory, to determine when to expected a fixed pattern to appear in a random sequence for the fist time. Using these expected values and comparing them with the observed values a randomness test can be defined. In this work patters are traced through the sequence in an overlapping manner.

Phase-space analysis of a novel cosmological model in f(Q) theory

The vanishing affine connections have been used solely while adopting the modified f(Q) gravity theory to the cosmology. Consequently, researchers could not get beyond what is already known in f(T) theory earlier. To alleviate this problem, in the present manuscript we investigate a recently proposed construction of f(Q) theory using non-vanishing affine connection in the spatially flat FLRW spacetime. We then investigate the cosmological solutions of f(Q) theory for a perfect fluid through the phase space analysis. We introduce few variables and dimensionless parameters to construct the corresponding equations suitable for the dynamical system approach. The conservation of the energy-momentum tensor leads to a constraint equation that relates the dynamical variables. Briefly, both unstable and stable de Sitter solutions appear which correspond to early and late times accelerated expansions. Also, unstable points corresponding to the matter dominated and radiation dominated eras have been found which do exist for every f(Q) function. As a result, the present discussion shows that f(Q) gravity endowed by non-vanishing affine connections is capable of explaining a true sequence of cosmic eras.

Groundwater environmental DNA metabarcoding reveals hidden diversity and reflects land-use and geology.

Despite being the most important source of liquid freshwater on the planet, groundwater is severely threatened by climate change, agriculture, or industrial mining. It is thus extensively monitored for pollutants and declines in quantity. The organisms living in groundwater, however, are rarely the target of surveillance programmes and little is known about the fauna inhabiting underground habitats. The difficulties accessing groundwater, the lack of expertise, and the apparent scarcity of these organisms challenge sampling and prohibit adequate knowledge on groundwater fauna. Environmental DNA (eDNA) metabarcoding provides an approach to overcome these limitations but is largely unexplored. Here, we sampled water in 20 communal spring catchment boxes used for drinking water provisioning in Switzerland, with a high level of replication at both filtration and amplification steps. We sequenced a portion of the COI mitochondrial gene, which resulted in 4917 ASVs, yet only 3% of the reads could be assigned to a species, genus, or family with more than 90% identity. Careful evaluation of the unassigned reads corroborated that these sequences were true COI sequences belonging mostly to diverse eukaryotic groups, not present in the reference databases. Principal component analyses showed a strong correlation of the community composition with the surface land-use (agriculture vs. forest) and geology (fissured rock vs. unconsolidated sediment). While incomplete reference databases limit the assignment of taxa in groundwater eDNA metabarcoding, we showed that taxonomy-free approaches can reveal large hidden diversity and couple it with major land-use drivers, revealing their imprint on chemical and biological properties of groundwater.

MAGDM Model Using the Exponential Similarity Measure of Neutrosophic Confidence Cubic Sets in a Single-Valued Neutrosophic Multivalued Circumstance

Probability estimation of small sample data is a key tool to ensure the probability that sample data fall within the confidence interval at a certain confidence level and probability distribution, which shows its advantages in practical engineering applications. Then, regarding a group decision-making (GDM) problem in the situation of indeterminacy and inconsistency, several experts/decision makers will assign several true, false, and indeterminate fuzzy values to the evaluation values of each alternative over different attributes, and then form a single-valued neutrosophic multivalued set (SvNMVS) as their assessed information. To ensure some confidence level of the evaluation values in the circumstance of SvNMVSs and GDM reliability, this paper aims to propose a conversion technique from SvNMVS to a neutrosophic confidence cubic set (NCCS) and a GDM model using the exponential similarity measure of NCCSs in the circumstance of SvNMVSs. First, we give the definition of NCCS, which is transformed from SvNMVS in terms of average values and confidence intervals of true, false, and indeterminate fuzzy sequences subject to the conditions of the normal distribution and confidence levels. Second, we present the exponential similarity measure of NCCSs and the weighted exponential similarity measure of NCCSs and their characteristics. Third, a GDM model is developed by using the weighted exponential similarity measure of NCCSs in the circumstance of SvNMVSs. Fourth, the developed GDM model is applied to a choice case of landslide treatment schemes in the circumstance of SvNMVSs to reveal its usability and suitability in actual GDM problems. Compared with the existing GDM models, the developed GDM model indicates its superiorities in decision flexibility and credibility/reliability subject to 90%, 95%, and 99% confidence levels.

Learning to Read and Write in the Language of Proteins

Learning to Read and Write in the Language of Proteins

AmpSeqR: an R package for amplicon deep sequencing data analysis

Amplicon sequencing (AmpSeq) is a methodology that targets specific genomic regions of interest for polymerase chain reaction (PCR) amplification so that they can be sequenced to a high depth of coverage. Amplicons are typically chosen to be highly polymorphic, usually with several highly informative, high frequency single nucleotide polymorphisms (SNPs) segregating in an amplicon of 100–200 base pair (bp). This allows high sensitivity detection and quantification of the frequency of each sequence within each sample making it suitable for applications such as low frequency somatic mosaicism detection or minor clone detection in mixed samples. AmpSeq is being increasingly applied to both biological and medical studies, in applications such as cancer, infectious diseases and brain mosaicism studies. Current bioinformatics pipelines for AmpSeq data processing lack downstream analysis, have difficulty distinguishing between true sequences and PCR sequencing errors and artifacts, and often require bioinformatic expertise. We present a new R package: AmpSeqR, designed for the processing of deep short-read amplicon sequencing data, with a focus on infectious diseases. The pipeline integrates several existing R packages combining them with newly developed functions to perform optimal filtering of reads to remove noise and improve the accuracy of the detected sequences data, permitting detection of very low frequency clones in mixed samples. The package provides useful functions including data pre-processing, amplicon sequence variants (ASVs) estimation, data post-processing, data visualization, and automatically generates a comprehensive Rmarkdown report that contains all essential results facilitating easy inclusion into reports and publications. AmpSeqR is publicly available at https://github.com/bahlolab/AmpSeqR.

Fast True Random Bit Generation With an SOA-Based Random Fiber Laser

Physical random bits are generated at rates up to 40 Gbps using an 8-bits digital oscilloscope acquiring the optical intensity at the output of an SOA-based random fiber laser. The simple and minimal off-line post-processing (the selection of 4 LBS) can be easily implemented in real time. The true random bits sequences passed all NIST tests.

Reduction of Gadolinium-Based Contrast Agents in MRI Using Convolutional Neural Networks and Different Input Protocols: Limited Interchangeability of Synthesized Sequences With Original Full-Dose Images Despite Excellent Quantitative Performance.

The purpose of this study was to implement a state-of-the-art convolutional neural network used to synthesize artificial T1-weighted (T1w) full-dose images from corresponding noncontrast and low-dose images (using various settings of input sequences) and test its performance on a patient population acquired prospectively. In this monocentric, institutional review board-approved study, a total of 138 participants were included who received an adapted imaging protocol with acquisition of a T1w low dose after administration of 10% of the standard dose and acquisition of a T1w full dose after administration of the remaining 90% of the standard dose of a gadolinium-containing contrast agent. A total of 83 participants formed the training sample (51.7 ± 16.5 years, 36 women), 25 the validation sample (55.3 ± 16.4 years, 11 women), and 30 the test sample (55.0 ± 15.0 years, 9 women). Four input settings were differentiated: only the T1w noncontrast and T1w low-dose images (standard setting), only the T1w noncontrast and T1w low-dose images with a prolonged postinjection time of 5 minutes (5-minute setting), multiple noncontrast sequences (T1w, T2w, diffusion) and the T1w low-dose images (extended setting), and only noncontrast sequences (T1w, T2w, diffusion) were used (zero-dose setting). For each setting, a deep neural network was trained to synthesize artificial T1w full-dose images, which were assessed on the test sample using an objective evaluation based on quantitative metrics and a subjective evaluation through a reader-based study. Three readers scored the overall image quality, the interchangeability in regard to the clinical conclusion compared with the true T1w full-dose sequence, the contrast enhancement of lesions, and their conformity to the respective references in the true T1w full dose. Quantitative analysis of the artificial T1w full-dose images of the standard setting provided a peak signal-to-noise ratio of 33.39 ± 0.62 (corresponding to an average improvement of the low-dose sequences of 5.2 dB) and a structural similarity index measure of 0.938 ± 0.005. In the 4-fold cross-validation, the extended setting yielded similar performance to the standard setting in terms of peak signal-to-noise ratio ( P = 0.20), but a slight improvement in structural similarity index measure ( P < 0.0001). For all settings, the reader study found comparable overall image quality between the original and artificial T1w full-dose images. The proportion of scans scored as fully or mostly interchangeable was 55%, 58%, 43%, and 3% and the average counts of false positives per case were 0.42 ± 0.83, 0.34 ± 0.71, 0.82 ± 1.15, and 2.00 ± 1.07 for the standard, 5-minute, extended, and zero-dose setting, respectively. Using a 5-point Likert scale (0 to 4, 0 being the worst), all settings of synthesized full-dose images showed significantly poorer contrast enhancement of lesions compared with the original full-dose sequence (difference of average degree of contrast enhancement-standard: -0.97 ± 0.83, P = <0.001; 5-minute: -0.93 ± 0.91, P = <0.001; extended: -0.96 ± 0.97, P = <0.001; zero-dose: -2.39 ± 1.14, P = <0.001). The average scores of conformity of the lesions compared with the original full-dose sequence were 2.25 ± 1.21, 2.22 ± 1.27, 2.24 ± 1.25, and 0.73 ± 0.93 for the standard, 5-minute, extended, and zero-dose setting, respectively. The tested deep learning algorithm for synthesis of artificial T1w full-dose sequences based on images after administration of only 10% of the standard dose of a gadolinium-based contrast agent showed very good quantitative performance. Despite good image quality in all settings, both false-negative and false-positive signals resulted in significantly limited interchangeability of the synthesized sequences with the original full-dose sequences.

CPViz: Visualizing clinical pathways represented in higher-order networks

To improve clinical care practice, it is important to understand the variability of clinical pathways executed in different contexts (e.g., pathways in different geographical locations, demographics, and phenotypic groups). A common way of representing clinical pathways is through network-based representations that capture trajectories of treatment steps. However, first-order networks, which are based on the Markovian property and the de facto standard model to represent transitions between steps, often fail to capture real trajectories. This paper introduces a visual analytic tool to explore and compare pathways represented in higher-order networks. Because each higher node in the network is a subtrajectory (i.e., partial or full history of treatment steps), the tool can display true sequences of treatment steps and compute the similarity of the two networks in a space of higher-order nodes. The tool also highlights areas in which the two networks are similar and dissimilar and how a certain subtrajectory is realized differently in different pathways. The paper demonstrates the tool's usefulness by applying it to multiple antidepressant pharmacotherapy pathways for veterans diagnosed with major depressive disorder and by illustrating heterogeneity in prescription patterns across pathways.

Accurate estimation of molecular counts from amplicon sequence data with unique molecular identifiers.

Amplicon sequencing is widely applied to explore heterogeneity and rare variants in genetic populations. Resolving true biological variants and quantifying their abundance is crucial for downstream analyses, but measured abundances are distorted by stochasticity and bias in amplification, plus errors during polymerase chain reaction (PCR) and sequencing. One solution attaches unique molecular identifiers (UMIs) to sample sequences before amplification. Counting UMIs instead of sequences provides unbiased estimates of abundance. While modern methods improve over naïve counting by UMI identity, most do not account for UMI reuse or collision, and they do not adequately model PCR and sequencing errors in the UMIs and sample sequences. We introduce Deduplication and Abundance estimation with UMIs (DAUMI), a probabilistic framework to detect true biological amplicon sequences and accurately estimate their deduplicated abundance. DAUMI recognizes UMI collision, even on highly similar sequences, and detects and corrects most PCR and sequencing errors in the UMI and sampled sequences. DAUMI performs better on simulated and real data compared to other UMI-aware clustering methods. Source code is available at https://github.com/DormanLab/AmpliCI. Supplementary data are available at Bioinformatics online.