Signal Sequence Research Articles

Identification of advanced optical modulation formatand estimation of signal-to-noise-ratio based on parallel-twin convolutional neural network

In this paper, we design a parallel-twin convolutional neural network (PT-CNN) deep learning model and use the signal constellation diagram to realize the identification of six advanced optical modulation formats (QPSK, 4QAM, 8PSK, 8QAM, 16PSK, 16QAM) and signal-to-noise-ratio (SNR) estimation. The influence of PT-CNN with different layers and kernel sizes is investigated and the optimal network model is chosen. Simulation results demonstrate that the proposed method has the advantages of not requiring manual feature extraction, having the ability to clearly distinguish the six modulation formats with 100% accuracy when SNR of the received signal sequences is higher than 12 dB. In addition, the high-accurate SNR estimation is realized simultaneously without increasing additional system complexity.

Algal rhodopsins encoding diverse signal sequence holds potential for expansion of organelle optogenetics.

Rhodopsins have been extensively employed for optogenetic regulation of bioelectrical activity of excitable cells and other cellular processes across biological systems. Various strategies have been adopted to attune the cellular processes at the desired subcellular compartment (plasma membrane, endoplasmic reticulum, Golgi, mitochondria, lysosome) within the cell. These strategies include-adding signal sequences, tethering peptides, specific interaction sites, or mRNA elements at different sites in the optogenetic proteins for plasma membrane integration and subcellular targeting. However, a single approach for organelle optogenetics was not suitable for the relevant optogenetic proteins and often led to the poor expression, mislocalization, or altered physical and functional properties. Therefore, the current study is focused on the native subcellular targeting machinery of algal rhodopsins. The N- and C-terminus signal prediction led to the identification of rhodopsins with diverse organelle targeting signal sequences for the nucleus, mitochondria, lysosome, endosome, vacuole, and cilia. Several identified channelrhodopsins and ion-pumping rhodopsins possess effector domains associated with DNA metabolism (repair, replication, and recombination) and gene regulation. The identified algal rhodopsins with diverse effector domains and encoded native subcellular targeting sequences hold immense potential to establish expanded organelle optogenetic regulation and associated cellular signaling.

High-Throughput IgG Reformatting and Expression Using Hybrid Secretion Signals and InTag Positive Selection Technology.

We have previously published protocols for high-throughput IgG reformatting and expression, that enable rapid reformatting of phage-displayed antibody Fab fragments into a single dual expression vector for full IgG expression in Expi293F cells (Chen et al. Nucleic Acids Res 42:e26, 2014; Chen et al. Methods in Molecular Biology, vol 1701, 2018). However, when working with phage clones from a naïve library containing highly diverse N-terminal sequences, where the 5' PCR primers bind, the PCR step can become cumbersome. To overcome this limitation, we have investigated and found that the C-terminal 7 amino acid residues of the human antibody VH1 secretion signal can be replaced with those from ompA or pelB bacterial signals to form hybrid signal sequences that can drive strong IgG expression in Expi293F cells. The use of such hybrid signals allows any Fab fragment in the library to be amplified and cloned into the IgG expression vector using only a single 5' PCR primer targeting the bacterial secretion signal of the light or heavy chain, thus dramatically simplifying the IgG reformatting workflow.

Strategic construction of mRNA vaccine derived from conserved and experimentally validated epitopes of avian influenza type A virus: a reverse vaccinology approach.

The development of vaccines that confer protection against multiple avian influenza A (AIA) virus strains is necessary to prevent the emergence of highly infectious strains that may result in more severe outbreaks. Thus, this study applied reverse vaccinology approach in strategically constructing messenger RNA (mRNA) vaccine construct against avian influenza A (mVAIA) to induce cross-protection while targeting diverse AIA virulence factors. Immunoinformatics tools and databases were utilized to identify conserved experimentally validated AIA epitopes. CD8+ epitopes were docked with dominant chicken major histocompatibility complexes (MHCs) to evaluate complex formation. Conserved epitopes were adjoined in the optimized mVAIA sequence for efficient expression in Gallus gallus. Signal sequence for targeted secretory expression was included. Physicochemical properties, antigenicity, toxicity, and potential cross-reactivity were assessed. The tertiary structure of its protein sequence was modeled and validated in silico to investigate the accessibility of adjoined B-cell epitope. Potential immune responses were also simulated in C-ImmSim. Eighteen experimentally validated epitopes were found conserved (Shannon index <2.0) in the study. These include one B-cell (SLLTEVETPIRNEWGCR) and 17 CD8+ epitopes, adjoined in a single mRNA construct. The CD8+ epitopes docked favorably with MHC peptide-binding groove, which were further supported by the acceptable ΔGbind (-28.45 to -40.59 kJ/mol) and Kd (<1.00) values. The incorporated Sec/SPI (secretory/signal peptidase I) cleavage site was also recognized with a high probability (0.964814). Adjoined B-cell epitope was found within the disordered and accessible regions of the vaccine. Immune simulation results projected cytokine production, lymphocyte activation, and memory cell generation after the 1st dose of mVAIA. Results suggest that mVAIA possesses stability, safety, and immunogenicity. In vitro and in vivo confirmation in subsequent studies are anticipated.

Model-Free Undetectable Attacks on Linear Systems Using LWE-Based Encryption

We show that the homomorphic property, a desired property in encrypted control, can lead to failure in the cyber defense of a dynamical control system from undetectable attacks, even though individual signal sequences remain unknown to the attacker. We consider an encryption method based on the Learning with Errors (LWE) problem and demonstrate how model-free undetectable attacks on linear systems over integers can be computed from sampled inputs and outputs that are encrypted. Previous work has shown that computing such attacks is possible on nonencrypted systems. Applying this earlier work to our scenario, with minor modifications, typically amplifies the error in encrypted messages unless a short vector problem is solved. Given that an attacker obtains a short vector, we derive the probability that the attack is detected and show how it explicitly depends on the encryption parameters. Finally, we simulate an attack obtained by our method on an encrypted linear system over integers and conduct an analysis of the probability that the attack will be detected.

Accurate Recognition of Natural language Using Machine Learning and Feature Fusion Processing

To enhance the performance of Chinese language pronunciation evaluation and speech recognition systems, researchers are focusing on developing intelligent techniques for multilevel fusion processing of data, features, and decisions using deep learning-based computer-aided systems. With a combination of score level, rank level, and hybrid level fusion, as well as fusion optimization and fusion score improvement, these systems can effectively combine multiple models and sensors to improve the accuracy of information fusion. Additionally, intelligent systems for information fusion, including those used in robotics and decision-making, can benefit from techniques such as multimedia data fusion and machine learning for data fusion. Furthermore, optimization algorithms and fuzzy approaches can be applied to data fusion applications in cloud environments and e-systems, while spatial data fusion can be used to enhance the quality of image and feature data In this paper, a new approach has been presented to identify the tonal language in continuous speech. This study proposes the Machine learning-assisted automatic speech recognition framework (ML-ASRF) for Chinese character and language prediction. Our focus is on extracting highly robust features and combining various speech signal sequences of deep models. The experimental results demonstrated that the machine learning neural network recognition rate is considerably higher than that of the conventional speech recognition algorithm, which performs more accurate human-computer interaction and increases the efficiency of determining Chinese language pronunciation accuracy.

Global–Local 3-D Convolutional Transformer Network for Hyperspectral Image Classification

Benefiting from powerful feature extraction capabilities, convolutional neural networks (CNNs) have gained prominence in hyperspectral image (HSI) classification. Nevertheless, with restricted receptive fields of convolution kernels, CNN-based methods fail to learn complex characteristics of long-range sequences. Meanwhile, vision transformer allows us to learn long-range dependencies in a global view, but local region features are ignored. To overcome these limitations, we propose a novel method entitled global-local three-dimensional convolutional transformer network (GTCT), where 3-D convolution is embedded in a dual-branch transformer to simultaneously capture global-local associations in both spectral and spatial domains. In particular, the global-local spectral convolutional transformer (GECT) is designed to exploit global spectral sequence signatures and local spectral relationships between bands. Symmetrically, the global-local spatial convolutional transformer (GACT) is devised to exploit local spatial context features and global interactions among different pixels. In addition, multiscale global-local spectral-spatial information is adaptively fused with trainable weights by the weighted multiscale spectral-spatial feature interaction (WMSFI) module. It is worth noting that a spectral-spatial global attention mechanism (SSGAM) is incorporated into multi-head convolutional attention to further integrate discriminative spectral-spatial information. Extensive experiments on four HSI datasets, including GF-5 and ZY1-02D satellite hyperspectral images, demonstrate the superiority of the proposed GTCT method over other state-of-the-art algorithms with fewer parameters and lower floating-point operations (FLOPs) in practical applications.

Sequence Similarity among Structural Repeats in the Piezo Family of Mechanosensitive Ion Channels

Members of the Piezo family of mechanically activated cation channels are involved in multiple physiological processes in higher eukaryotes, including vascular development, cell differentiation, touch perception, hearing, and more, but they are also common in single-celled eukaryotic microorganisms. Mutations in these proteins in humans are associated with a variety of diseases, such as colorectal adenomatous polyposis, dehydrated hereditary stomatocytosis, and hereditary xerocytosis. Available 3D structures for Piezo proteins show nine regions of four transmembrane segments each that have the same fold. Despite the remarkable similarity among the nine characteristic structural repeats in the family, no significant sequence similarity among them has been reported. Using bioinformatics approaches and the Transporter Classification Database (TCDB) as reference, we reliably identified sequence similarity among repeats based on four lines of evidence: (1) hidden Markov model-profile similarities across repeats at the family level, (2) pairwise sequence similarities between different repeats across Piezo homologs, (3) Piezo-specific conserved sequence signatures that consistently identify the same regions across repeats, and (4) conserved residues that maintain the same orientation and location in 3D space.

Nuclear translocation of MLKL enhances necroptosis by a RIP1/RIP3-independent mechanism in H9c2 cardiomyoblasts

Accumulating evidence suggests that necroptosis of cardiomyocytes contributes to cardiovascular diseases. Lethal disruption of the plasma membrane in necroptosis is induced by oligomers of mixed lineage kinase domain-like (MLKL) that is translocated to the membrane from the cytosol. However, the role played by cytoplasmic-nuclear shuttling of MLKL is unclear. Here, we tested the hypothesis that translocation of MLKL to the nucleus promotes the necroptosis of cardiomyocytes. Activation of the canonical necroptotic signaling pathway by a combination of TNF-α and zVAD (TNF/zVAD) increased nuclear MLKL levels in a RIP1-activity-dependent manner in H9c2 cells, a rat cardiomyoblast cell line. By use of site-directed mutagenesis, we found a nuclear export signal sequence in MLKL and prepared its mutant (MLKL-L280/283/284A), though a search for a nuclear import signal was unsuccessful. MLKL-L280/283/284A localized to both the cytosol and the nucleus. Expression of MLKL-L280/283/284A induced necroptotic cell death, which was attenuated by GppNHp, an inhibitor of Ran-mediated nuclear import, but not by inhibition of RIP1 activity or knockdown of RIP3 expression. GppNHp partly suppressed H9c2 cell death induced by TNF/zVAD treatment. These results suggest that MLKL that is translocated to the nucleus via RIP1-mediated necroptotic signaling enhances the necroptosis of cardiomyocytes through a RIP1-/RIP3-independent mechanism.

Covariance recovery for one-bit sampled stationary signals with time-varying sampling thresholds

One-bit quantization, which relies on comparing the signals of interest with given threshold levels, has attracted considerable attention in signal processing for communications and sensing. A useful tool for covariance recovery in such settings is the arcsine law, that estimates the normalized covariance matrix of zero-mean stationary input signals. This relation, however, only considers a zero sampling threshold, which can cause a remarkable information loss. In this paper, the idea of the arcsine law is extended to the case where one-bit analog-to-digital converters (ADCs) apply time-varying thresholds. Specifically, three distinct approaches are proposed, investigated, and compared, to recover the autocorrelation sequence of the stationary signals of interest. Additionally, we will study a modification of the Bussgang law, a famous relation facilitating the recovery of the cross-correlation between the one-bit sampled data and the zero-mean stationary input signal. Similar to the case of the arcsine law, the Bussgang law only considers a zero sampling threshold. This relation is also extended to accommodate the more general case of time-varying thresholds for the stationary input signals.

Influence of Dosing Regimen and Adjuvant Type on the Immunogenicity of Novel Recombinant Zika Virus-Like Particles.

Zika virus (ZIKV) is a reemerging mosquito-borne flavivirus that causes febrile illness and is also linked to Guillain-Barré syndrome as well as to microcephaly in newborns. Due to the risk of fetuses developing microcephaly, ZIKV is a serious problem for pregnant women. Although different types of vaccine antigens have been investigated, there is still no approved vaccine that prevents ZIKV. The aim of this study was to produce a potential anti-Zika virus vaccine candidate based on virus-like particles (VLPs) in mammalian cells and to analyze the role of dosing regimen and adjuvant type on the immunogenicity of the obtained antigen. Novel recombinant VLPs (F2A) were designed by introducing the optimized signal sequence of prM protein and by adding a self-cleavage peptide 2A between proteins prM and E. These modifications improved the formation of the glycoprotein E dimer. It has been shown that the increasing dosing regimen generates a significantly higher titer of antibodies; however, the adjuvant type does not affect this process. Sera from mice immunized using an increasing dosing schedule also showed higher neutralization activity against both Zika strains (H/PAN/2016/BEI-259634, a pandemic strain belonging to Asian lineage, and MR766, a reference strain from African lineage). In summary, this is the first report showing the influence of vaccination schedules and adjuvants on the immunogenicity of ZIKV virus-like particles. IMPORTANCE Considering the transmission of ZIKV and the risk of another epidemic as well as the neurological complications that follow ZIKV infection, the virus remains a serious problem for the human population, especially pregnant women. Therefore, there is a great need to develop new effective vaccine candidates. Although different types of vaccine antigens have been used in preclinical studies worldwide, there is still no approved vaccine to prevent ZIKV. VLPs are among the most potent antigens, but to use VLPs, adjuvants must be added to the formulation and appropriate administration must be performed. In this study, we show for the first time the influence of vaccination schedules and adjuvants on the immunogenicity of recombinant ZIKV VLPs. The obtained results can be used in new vaccine designs not only against ZIKV but also against other important viral pathogens.

A Hands-on Activity for V(D)J Recombination in an Undergraduate Immunology Class.

This learning activity teaches the difficult concept of V(D)J recombination as it occurs in B cells. Following the traditional lecture, this hands-on activity uses pipe cleaners of various colors representing variable, joining, and diversity gene segments and recombination signal sequences. Students are provided with instructions for using the pipe cleaners to assemble specific light and heavy immunoglobulin chains. Students each assemble their own light and heavy chains and compare the products made by classmates. This activity uses materials that are easy and affordable to acquire and provides a tactile approach to reinforcing concepts that students often struggle to visualize and master from lecture and textbook material alone.

The α-mating factor secretion signals and endogenous signal peptides for recombinant protein secretion in Komagataella phaffii

BackgroundThe budding yeast Komagataella phaffii (Pichia pastoris) is widely employed to secrete proteins of academic and industrial interest. For secretory proteins, signal peptides are the sorting signal to direct proteins from cytosol to extracellular matrix, and their secretion efficiency directly impacts the yields of the targeted proteins in fermentation broth. Although the α-mating factor (MF) secretion signal from S. cerevisiae, the most common and widely used signal sequence for protein secretion, works in most cases, limitation exists as some proteins cannot be secreted efficiently. As the optimal choice of secretion signals is often protein specific, more secretion signals need to be developed to augment protein expression levels in K. phaffii.ResultsIn this study, the secretion efficiency of 40 α-MF secretion signals from various yeast species and 32 endogenous signal peptides from K. phaffii were investigated using enhanced green fluorescent protein (EGFP) as the model protein. All of the evaluated α-MF secretion signals successfully directed EGFP secretion except for the secretion signals of the yeast D. hansenii CBS767 and H. opuntiae. The secretion efficiency of α-MF secretion signal from Wickerhamomyces ciferrii was higher than that from S. cerevisiae. 24 out of 32 endogenous signal peptides successfully mediated EGFP secretion. The signal peptides of chr3_1145 and FragB_0048 had similar efficiency to S. cerevisiae α-MF secretion signal for EGFP secretion and expression.ConclusionsThe screened α-MF secretion signals and endogenous signal peptides in this study confer an abundance of signal peptide selection for efficient secretion and expression of heterologous proteins in K. phaffii.

Exploring in vitro expression and immune potency in mice using mRNA encoding the Plasmodium falciparum malaria antigen, CelTOS.

The secreted malarial protein, Cell-Traversal protein for Ookinetes and Sporozoites (CelTOS), is highly conserved among Plasmodium species, and plays a role in the invasion of mosquito midgut cells and hepatocytes in the vertebrate host. CelTOS was identified as a potential protective antigen based on a proteomic analysis, which showed that CelTOS stimulated significant effector T cells producing IFN-γ in peripheral blood mononuclear cells (PBMCs) from radiation attenuated sporozoite-immunized, malaria-naïve human subjects. In a rodent malaria model, recombinant full-length CelTOS protein/adjuvant combinations induced sterile protection, and in several studies, functional antibodies were produced that had hepatocyte invasion inhibition and transmission-blocking activities. Despite some encouraging results, vaccine approaches using CelTOS will require improvement before it can be considered as an effective vaccine candidate. Here, we report on the use of mRNA vaccine technology to induce humoral and cell-mediated immune responses using this antigen. Several pfceltos encoding mRNA transcripts were assessed for the impact on protein translation levels in vitro. Protein coding sequences included those to evaluate the effects of signal sequence, N-glycosylation on translation, and of nucleoside substitutions. Using in vitro transfection experiments as a pre-screen, we assessed the quality of the expressed CelTOS target relative to the homogeneity, cellular localization, and durability of expression levels. Optimized mRNA transcripts, which demonstrated highest protein expression levels in vitro were selected for encapsulation in lipid nanoparticles (LNP) and used to immunize mice to assess for both humoral and cellular cytokine responses. Our findings indicate that mRNA transcripts encoding pfceltos while potent for inducing antigen-specific cellular cytokine responses in mice, were less able to mount PfCelTOS-specific antibody responses using a two-dose regimen. An additional booster dose was needed to overcome low seroconversion rates in mice. With respect to antibody fine specificities, N-glycosylation site mutated immunogens yielded lower immune responses, particularly to the N-terminus of the molecule. While it remains unclear the impact on CelTOS antigen as immunogen, this study highlights the need to optimize antigen design for vaccine development.

A potential role for SARS-CoV-2 small viral RNAs in targeting host microRNAs and modulating gene expression

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease (COVID-19) in humans, with symptoms ranging from mild to severe, including fatality. The molecular mechanisms surrounding the effects of viral infection on the host RNA machinery remain poorly characterized. We used a comparative transcriptomics approach to investigate the effects of SARS-CoV-2 infection on the host mRNA and sRNA expression machinery in a human lung epithelial cell line (Calu-3) and an African green monkey kidney cell line (Vero-E6). Upon infection, we observed global changes in host gene expression and differential expression of dozens of host miRNAs, many with known links to viral infection and immune response. Additionally, we discovered an expanded landscape of more than a hundred SARS-CoV-2-derived small viral RNAs (svRNAs) predicted to interact with differentially expressed host mRNAs and miRNAs. svRNAs are derived from distinct regions of the viral genome and sequence signatures suggest they are produced by a non-canonical biogenesis pathway. 52 of the 67 svRNAs identified in Calu-3 cells are predicted to interact with differentially expressed miRNAs, with many svRNAs having multiple targets. Accordingly, we speculate that these svRNAs may play a role in SARS-CoV-2 propagation by modulating post-transcriptional gene regulation, and that methods for antagonizing them may have therapeutic value.

Detection of chromosomal alteration after infusion of gene-edited allogeneic CAR T cells

A chromosome 14 inversion was found in a patient who developed bone marrow aplasia following treatment with allogeneic chimeric antigen receptor (CAR) Tcells containing gene edits made with transcription activator-like effector nucleases (TALEN). TALEN editing sites were not involved at either breakpoint. Recombination signal sequences (RSSs) were found suggesting recombination-activating gene (RAG)-mediated activity. The inversion represented a dominant clone detected in the context of decreasing absolute CAR Tcell and overall lymphocyte counts. The inversion was not associated with clinical consequences and wasnot detected in the drug product administered to this patient or in any drug product used in this or other trials using the same manufacturing processes. Neither was the inversion detected in this patient at earlier time points or in any other patient enrolled in this or other trials treated with this or other product lots. This case illustrates that spontaneous, possibly RAG-mediated, recombination events unrelated to gene editing can occur in adoptive cell therapy studies, emphasizes the need for ruling out off-target gene editing sites, and illustrates that other processes, such as spontaneous V(D)J recombination, can lead to chromosomal alterations in infused cells independent of gene editing.

Generation of Multiple Arbovirus-like Particles Using a Rapid Recombinant Vaccinia Virus Expression Platform.

As demonstrated by the 2015 Zika virus outbreak in the Americas, emerging and re-emerging arboviruses are public health threats that warrant research investment for the development of effective prophylactics and therapeutics. Many arboviral diseases are underreported, neglected, or of low prevalence, yet they all have the potential to cause outbreaks of local and international concern. Here, we show the production of virus-like particles (VLPs) using a rapid and efficient recombinant vaccinia virus (VACV) expression system for five tick- and mosquito-borne arboviruses: Powassan virus (POWV), Heartland virus (HRTV), severe fever with thrombocytopenia syndrome virus (SFTSV), Bourbon virus (BRBV) and Mayaro virus (MAYV). We detected the expression of arbovirus genes of interest by Western blot and observed the expression of VLPs that resemble native virions under transmission electron microscopy. We were also able to improve the secretion of POWV VLPs by modifying the signal sequence within the capsid gene. This study describes the use of a rapid VACV platform for the production and purification of arbovirus VLPs that can be used as subunit or vectored vaccines, and provides insights into the selection of arbovirus genes for VLP formation and genetic modifications to improve VLP secretion and yield.

Navigating and expanding the roadmap of natural product genome mining tools.

Natural products are structurally highly diverse and exhibit a wide array of biological activities. As a result, they serve as an important source of new drug leads. Traditionally, natural products have been discovered by bioactivity-guided fractionation. The advent of genome sequencing technology has resulted in the introduction of an alternative approach towards novel natural product scaffolds: Genome mining. Genome mining is an in-silico natural product discovery strategy in which sequenced genomes are analyzed for the potential of the associated organism to produce natural products. Seemingly universal biosynthetic principles have been deciphered for most natural product classes that are used to detect natural product biosynthetic gene clusters using pathway-encoded conserved key enzymes, domains, or motifs as bait. Several generations of highly sophisticated tools have been developed for the biosynthetic rule-based identification of natural product gene clusters. Apart from these hard-coded algorithms, multiple tools that use machine learning-based approaches have been designed to complement the existing genome mining tool set and focus on natural product gene clusters that lack genes with conserved signature sequences. In this perspective, we take a closer look at state-of-the-art genome mining tools that are based on either hard-coded rules or machine learning algorithms, with an emphasis on the confidence of their predictions and potential to identify non-canonical natural product biosynthetic gene clusters. We highlight the genome mining pipelines' current strengths and limitations by contrasting their advantages and disadvantages. Moreover, we introduce two indirect biosynthetic gene cluster identification strategies that complement current workflows. The combination of all genome mining approaches will pave the way towards a more comprehensive understanding of the full biosynthetic repertoire encoded in microbial genome sequences.

Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication

Orthogonal time frequency space (OTFS) is a novel two-dimensional (2D) modulation technique that provides reliable communications over time- and frequency-selective channels. In underwater acoustic (UWA) channel, the multi-path delay and Doppler shift are several magnitudes larger than wireless radio communication, which will cause severe time- and frequency-selective fading. The receiver has to recover the distorted OTFS signal with inter-symbol interference (ISI) and inter-carrier interference (ICI). The conventional UWA OTFS receivers perform channel estimation explicitly and equalization to detect transmitted symbols, which requires prior knowledge of the system. This paper proposes a deep learning-based signal detection method for UWA OTFS communication, in which the deep neural network can recover the received symbols after sufficient training. In particular, it cascades a convolutional neural network (CNN) with skip connections (SC) and a bidirectional long short-term memory (BiLSTM) network to perform signal recovery. The proposed method extracts feature information from received OTFS signal sequences and trains the neural network for signal detection. The numerical results demonstrate that the SC-CNN-BiLSTM-based OTFS detection method performs with a lower bit error rate (BER) than the 2D-CNN, FC-DNN, and conventional signal detection methods.

OrganelX web server for sub-peroxisomal and sub-mitochondrial protein localization and peroxisomal target signal detection

We present the OrganelX e-Science Web Server that provides a user-friendly implementation of the In-Pero and In-Mito classifiers for sub-peroxisomal and sub-mitochondrial localization of peroxisomal and mitochondrial proteins and the Is-PTS1 algorithm for detecting and validating potential peroxisomal proteins carrying a PTS1 signal sequence. The OrganelX e-Science Web Server is available at https://organelx.hpc.rug.nl/fasta/.