The lifetime of a sensor network is generally longer than that of a single sensor node, so to ensure the connectivity of the network, new nodes need to be deployed into the network in multiphase. Such networks are called multiphase sensor networks. In sensor networks, establishing a pairwise key between two neighboring nodes is a fundamental security requirement. However, for sensor nodes with strictly limited capabilities, there are great challenges in establishing a pairwise key between two neighboring sensor nodes deployed at different phases. In order to solve this problem, some scholars have proposed some solutions based on backward key-chains and forward key-chains, however, how to further improve the networks’ resilience against node capture attacks still needs further research; and some scholars have proposed some solutions based on key pools of each phase are independent, but how to improve the probability of establishing a pairwise key between two neighboring nodes deployed at different phases is still a very challenging problem. In this article, we propose a new pairwise key scheme based on deployment knowledge. Theoretical analysis and simulation show that the proposed model can achieve high probability of establishing a pairwise key between two neighboring nodes and good resilience against node capture attacks by setting appropriate parameters.

Sensor networks are often deployed outdoors and are more susceptible to various attacks. In order to protect communication between nodes, scholars have proposed key management schemes. The most popular among them is the key predistribution scheme. This scheme has the following contradiction: in order to improve security, the number of keys predistributed to nodes should be minimized as much as possible. However, as the number of predistribution keys decreases, the network may no longer be securely connected, resulting in wastage of nodes. In this letter, we propose a new key establishment method to address this issue. Analysis and simulation show that the proposed scheme can reduce the number of predistribution keys in the original schemes while ensuring secure network connectivity.

In multi-phase sensor networks, each deployment is called a phase of networks, and how to improve the secure connectivity between nodes deployed at different phases is a key issue. But this is not an easy task for resource-constrained sensor nodes. Recently, some scholars have proposed some methods where nodes deployed at the same phase can establish shared keys directly, which offers perfect resilience against destruction. Additionally, they introduced an online key update method to establish shared keys for nodes deployed at different phases. However, the resilience of this scheme decreases rapidly as the number of newly deployed nodes captured during the key establishment phase increases. In this paper, we propose a T-type key pre-distribution scheme based on deployment knowledge, and also introduce a novel method for path key establishment. In this scheme, the shared keys established directly between nodes exhibit perfect resilience. Furthermore, this scheme does not require the assumption of a relatively secure time period during the key establishment phase. In this proposed scheme, by setting appropriate parameters, theoretical analysis and simulation show that the probability of two neighboring nodes establishing a shared key is about 1, and the resilience against node capture attacks is excellent.

MOTIVATION: Tandem mass spectrometry is an essential technology for characterizing chemical compounds at high sensitivity and throughput, and is commonly adopted in many fields. However, computational methods for automated compound identification from their MS/MS spectra are still limited, especially for novel compounds that have not been previously characterized. In recent years, in silico methods were proposed to predict the MS/MS spectra of compounds, which can then be used to expand the reference spectral libraries for compound identification. However, these methods did not consider the compounds' 3D conformations, and thus neglected critical structural information. RESULTS: We present the 3D Molecular Network for Mass Spectra Prediction (3DMolMS), a deep neural network model to predict the MS/MS spectra of compounds from their 3D conformations. We evaluated the model on the experimental spectra collected in several spectral libraries. The results showed that 3DMolMS predicted the spectra with the average cosine similarity of 0.691 and 0.478 with the experimental MS/MS spectra acquired in positive and negative ion modes, respectively. Furthermore, 3DMolMS model can be generalized to the prediction of MS/MS spectra acquired by different labs on different instruments through minor fine-tuning on a small set of spectra. Finally, we demonstrate that the molecular representation learned by 3DMolMS from MS/MS spectra prediction can be adapted to enhance the prediction of chemical properties such as the elution time in the liquid chromatography and the collisional cross section measured by ion mobility spectrometry, both of which are often used to improve compound identification. AVAILABILITY AND IMPLEMENTATION: The codes of 3DMolMS are available at https://github.com/JosieHong/3DMolMS and the web service is at https://spectrumprediction.gnps2.org.

Unattended wireless sensor networks (UWSNs), in which mobile sinks (MSs) are responsible for the data collection, and which are vulnerable to multiple attacks. For example, the adversaries can initiate MS replication attack after compromising a large number of sensor nodes (SNs). To resist such attacks, some scholars have proposed some schemes. In these schemes, it is assumed that MSs are equipped with tamper resistance hardware, so they are pre-distributed most of keys of a key pool for achieving authentication with SNs. However, in outdoor and even sensitive areas, tamper-resistant hardware is not always absolutely safe. Once MSs are compromised, networks become insecure. In this paper, we propose a secure model. The model contains three types of nodes, namely SNs, MSs, and a base station (BS). MSs are responsible for collecting the data encrypted by SNs and forwarding it to BS; BS is responsible for decrypting and analyzing the collected data. During the data collection process, an MS can collect an SN's data only after being authenticated by the SN using the pre-distribution key information. But it cannot decrypt the collected data. In this model, the adversaries can induce a new type of false data injection attack. In the attack, the replicated MSs impersonate uncompromised SNs to send false data to BS by using the compromised key information. If BS accepts a large amount of false data, it will make a wrong judgment. Analysis and simulation show that the proposed model has excellent resistance against MS replication attacks and false data injection attacks by setting appropriate parameter values.

To achieve security in wireless sensor networks (WSNs), it is important to be able to encrypt messages sent among sensor nodes by using shared keys between them. Due to resource constraints, achieving such key agreement in WSNs is nontrivial. Previous research indicates that key management schemes using deployment knowledge can significantly improve the performance of WSNs. Nevertheless, in these schemes, resilient local connectivity and resilient global connectivity become unstable when deployment error changes. To resolve the above problem, in this article, a one-way associated key management model is proposed. In this model, the key pool consists of two layers: 1) the global layer and 2) the local layer. According to different deployment errors, the number of keys allocated from the global key pool and local key pools can be dynamically adjusted, thereby improving the stability of networks' performance. In multiphase sensor networks, analysis and simulation indicate that our scheme has better adaptability in applications where deployment error changes as compared with related schemes.

Unattended wireless sensor networks (UWSNs) are vulnerable to mobile sink (MS) replication attack. In this attack, using the compromised key information, an attacker can collect data from networks by impersonating sinks. To resist such an attack, some schemes have been proposed. To improve the resilience of MS replication attack of these schemes, we can integrate them with schemes based on deployment knowledge. However, there are the following defects: 1) the probability of mutual authentication between a MS and a sensor node is less than 1 and 2) during the authentication phase, the energy consumption of sensor nodes increases significantly as the deployment area expands. In this paper, we construct 3-D backward key chains based on deployment knowledge and propose a new authentication scheme based on these. As compared with these existing related schemes, the detailed theory analysis and simulation results indicate that the scheme can ensure that a MS can be authenticated by sensor nodes, and can improve the resilience of networks' MS replication attack with low energy consumption.

Motivation: Rapid advancement in high throughput genome and transcriptome sequencing (HTS) and mass spectrometry (MS) technologies has enabled the acquisition of the genomic, transcriptomic and proteomic data from the same tissue sample. We introduce a computational framework, ProTIE, to integratively analyze all three types of omics data for a complete molecular profile of a tissue sample. Our framework features MiStrVar, a novel algorithmic method to identify micro structural variants (microSVs) on genomic HTS data. Coupled with deFuse, a popular gene fusion detection method we developed earlier, MiStrVar can accurately profile structurally aberrant transcripts in tumors. Given the breakpoints obtained by MiStrVar and deFuse, our framework can then identify all relevant peptides that span the breakpoint junctions and match them with unique proteomic signatures. Observing structural aberrations in all three types of omics data validates their presence in the tumor samples. Results: We have applied our framework to all The Cancer Genome Atlas (TCGA) breast cancer Whole Genome Sequencing (WGS) and/or RNA-Seq datasets, spanning all four major subtypes, for which proteomics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) have been released. A recent study on this dataset focusing on SNVs has reported many that lead to novel peptides. Complementing and significantly broadening this study, we detected 244 novel peptides from 432 candidate genomic or transcriptomic sequence aberrations. Many of the fusions and microSVs we discovered have not been reported in the literature. Interestingly, the vast majority of these translated aberrations, fusions in particular, were private, demonstrating the extensive inter-genomic heterogeneity present in breast cancer. Many of these aberrations also have matching out-of-frame downstream peptides, potentially indicating novel protein sequence and structure. Availability and implementation: MiStrVar is available for download at https://bitbucket.org/compbio/mistrvar, and ProTIE is available at https://bitbucket.org/compbio/protie. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

In order to find evidence for translation of alternatively spliced transcripts, especially those that result in a change in reading frame, we collected exon-skipping cases previously found by RNA-Seq and applied a computational approach to screen millions of mass spectra. These spectra came from seven human and six mouse tissues, five of which are the same between the two organisms: liver, kidney, lung, heart, and brain. Overall, we detected 4 percent of all exon-skipping events found in RNA-seq data, regardless of their effect on reading frame. The fraction of alternative isoforms detected did not differ between out-of-frame and in-frame events. Moreover, the fraction of identified alternative exon-exon junctions and constitutive junctions were similar. Together, our results suggest that both in-frame and out-of-frame translation may be actively used to regulate protein activity or localization.

Metaproteomic studies adopt the common bottom-up proteomics approach to investigate the protein composition and the dynamics of protein expression in microbial communities. When matched metagenomic and/or metatranscriptomic data of the microbial communities are available, metaproteomic data analyses often employ a metagenome-guided approach, in which complete or fragmental protein-coding genes are first directly predicted from metagenomic (and/or metatranscriptomic) sequences or from their assemblies, and the resulting protein sequences are then used as the reference database for peptide/protein identification from MS/MS spectra. This approach is often limited because protein coding genes predicted from metagenomes are incomplete and fragmental. In this paper, we present a graph-centric approach to improving metagenome-guided peptide and protein identification in metaproteomics. Our method exploits the de Bruijn graph structure reported by metagenome assembly algorithms to generate a comprehensive database of protein sequences encoded in the community. We tested our method using several public metaproteomic datasets with matched metagenomic and metatranscriptomic sequencing data acquired from complex microbial communities in a biological wastewater treatment plant. The results showed that many more peptides and proteins can be identified when assembly graphs were utilized, improving the characterization of the proteins expressed in the microbial communities. The additional proteins we identified contribute to the characterization of important pathways such as those involved in degradation of chemical hazards. Our tools are released as open-source software on github at https://github.com/COL-IU/Graph2Pro.

Nowadays proteomics becomes more and more popular in life science. Protein quantification, especially based on mass spectrometry (short for MS) method, is perceived as an essential part of research on proteomics. There have been some algorithms and software for protein quantification based on MS. But they have difficulties on portability, applicability and longtime running. To solve these problems, we developed a new domestic parallel software package called QuantWiz for high performance liquid chromatography (short for LC)-MS-based label-free protein quantification. In this paper, we described the framework design and prototype development of this high performance software package firstly. Also, user interface developed for the visualization of QuantWiz is introduced. Finally, we showed implementation of the parallelization version and performance of some experiments on this software package.