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DNA data storage is a rapidly developing technology with great potential due to its high density, long-term durability, and low maintenance cost. The major technical challenges include various errors, such as strand breaks, rearrangements, and indels that frequently arise during DNA synthesis, amplification, sequencing, and preservation. In this study, a de novo strand assembly algorithm (DBGPS) is developed using de Bruijn graph and greedy path search to meet these challenges. DBGPS shows substantial advantages in handling DNA breaks, rearrangements, and indels. The robustness of DBGPS is demonstrated by accelerated aging, multiple independent data retrievals, deep error-prone PCR, and large-scale simulations. Remarkably, 6.8 MB of data is accurately recovered from a severely corrupted sample that has been treated at 70 °C for 70 days. With DBGPS, we are able to achieve a logical density of 1.30 bits/cycle and a physical density of 295 PB/g. DNA data storage is a rapidly developing technology with great potential due to its high density, long-term durability, and low maintenance cost. Here the authors present a strand assembly algorithm (DBGPS) using de Bruijn graph and greedy path search. [ABSTRACT FROM AUTHOR]

Accurate identification of protein-nucleotide binding residues is crucial for the study of drug structure and protein functional annotation. The study of protein-nucleotide binding residues is a typical problem of sample imbalance. The minority class (binding residues) are far less than the majority class (non-binding residues). The traditional machine learning algorithm is not universal for this kind of research, the results will be seriously biased to majority class. To deal with the serious imbalance problem, we propose a new computational method to identify protein-nucleotide binding residues via Graph Regularized k-local Hyperplane Distance Nearest Neighbor (GHKNN). On the training set, we compare the performance of the basic classifier, the ensemble classifier and the single classifier. On the independent test sets, we compare the performance with other existing models. The experimental results prove that our proposed method has higher accuracy in the identification of protein-nucleotide binding residues and is more prominent than other existing models. The data and material are freely available at https://github.com/guofei-tju/GHKNN. [ABSTRACT FROM AUTHOR]

Ricin is a toxic protein derived from castor beans and composed of a cytotoxic A chain and a galactose-binding B chain linked by a disulfide bond, which can inhibit protein synthesis and cause cell death. Owing to its high toxicity, ease of preparation, and lack of medical countermeasures, ricin has been listed as both chemical and biological warfare agents. For homeland security or public safety, the unambiguous, sensitive, and rapid methods for identification and quantification of ricin in complicated matrices are of urgent need. Mass spectrometric analysis, which provides specific and sensitive characterization of protein, can be applied to confirm and quantify ricin. Here, we report a liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) method in which ricin was extracted and enriched from serum by immunocapture using anti-ricin monoclonal antibody 3D74 linked to magnetic beads, then digested by trypsin, and analyzed by LC-ESI-MS/MS. Among 19 distinct peptides observed in LC-quadrupole/time of flight-MS (LC-QTOF-MS), two specific and sensitive peptides, T (VGLPINQR) and T (DNCLTSDSNIR), were chosen, and a highly sensitive determination of ricin was established in LC-triple quadrupole-MS (LC-QqQ-MS) operating in multiple reaction monitoring mode. These specific peptides can definitely distinguish ricin from the homologous protein Ricinus communis agglutinin (RCA120), even though the amino acid sequence homology of the A-chain of ricin and RCA120 is up to ca. 93 % and that of B-chain is ca. 85 %. Furthermore, peptide T was preferred in the quantification of ricin because its sensitivity was at least one order of magnitude higher than that of the peptide T. Combined with immunocapture enrichment, this method provided a limit of detection of ca. 2.5 ng/mL and the limit of quantification was ca. 5 ng/mL of ricin in serum, respectively. Both precision and accuracy of this method were determined and the RSD was less than 15 %. This established method was then applied to measure ricin in serum samples collected from rats exposed to ricin at the dosage of 50 μg/kg in an intravenous injection manner. The results showed that ca. 10 ng/mL of the residual ricin in poisoned rats serum could be detected even at 12 h after exposure. [ABSTRACT FROM AUTHOR]

Transcription factors (TFs) binding to specific DNA sequences or motifs, are elementary to the regulation of transcription. The gene is regulated by a combination of TFs in close proximity. Analysis of co-TFs is an important problem in understanding the mechanism of transcriptional regulation. Recently, ChIP-seq in mapping TF provides a large amount of experimental data to analyze co-TFs. Several studies show that if two TFs are co-associated, the relative distance between TFs exhibits a peak-like distribution. In order to analyze co-TFs, we develop a novel method to evaluate the associated situation between TFs. We design an adjacency score based on ordered differences, which can illustrate co-TF binding affinities for motif analysis. For all candidate motifs, we calculate corresponding adjacency scores, and then list descending-order motifs. From these lists, we can find co-TFs for candidate motifs. On ChIP-seq datasets, our method obtains best AUC results on five datasets, 0.9432 for NMYC, 0.9109 for KLF4, 0.9006 for ZFX, 0.8892 for ESRRB, 0.8920 for E2F1. Our method has great stability on large sample datasets. AUC results of our method on all datasets are above 0.8. [ABSTRACT FROM AUTHOR]