Many software tools have been developed for the automated identification of peptides from tandem mass spectra. The accuracy and sensitivity of the identification software via database search are critical for successful proteomics experiments. A new database search tool, PEAKS DB, has been developed by incorporating the de novo sequencing results into the database search. PEAKS DB achieves significantly improved accuracy and sensitivity over two other commonly used software packages. Additionally, a new result validation method, decoy fusion, has been introduced to solve the issue of overconfidence that exists in the conventional target decoy method for certain types of peptide identification software.

In this paper we investigate the use of deep neural networks (DNNs) for a small footprint text-dependent speaker verification task. At development stage, a DNN is trained to classify speakers at the framelevel. During speaker enrollment, the trained DNN is used to extract speaker specific features from the last hidden layer. The average of these speaker features, or d-vector, is taken as the speaker model. At evaluation stage, a d-vector is extracted for each utterance and compared to the enrolled speaker model to make a verification decision. Experimental results show the DNN based speaker verification system achieves good performance compared to a popular i-vector system on a small footprint text-dependent speaker verification task. In addition, the DNN based system is more robust to additive noise and outperforms the i-vector system at low False Rejection operating points. Finally the combined system outperforms the i-vector system by 14% and 25% relative in equal error rate (EER) for clean and noisy conditions respectively.Index Terms-Deep neural networks, speaker verification.

Patients with retinal degeneration lose sight due to gradual demise of photoreceptors. Electrical stimulation of the surviving retinal neurons provides an alternative route for delivery of visual information. We demonstrate that subretinal arrays with 70 μm photovoltaic pixels provide highly localized stimulation, with electrical and visual receptive fields of comparable sizes in rat retinal ganglion cells. Similarly to normal vision, retinal response to prosthetic stimulation exhibits flicker fusion at high frequencies, adaptation to static images and non-linear spatial summation. In rats with retinal degeneration, these photovoltaic arrays provide spatial resolution of 64 ± 11 μm, corresponding to half of the normal visual acuity in pigmented rats. Ease of implantation of these wireless and modular arrays, combined with their high resolution opens the door to functional restoration of sight.

De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing. We evaluated the method on a wide variety of species and found that DeepNovo considerably outperformed state of the art methods, achieving 7.7-22.9% higher accuracy at the amino acid level and 38.1-64.0% higher accuracy at the peptide level. We further used DeepNovo to automatically reconstruct the complete sequences of antibody light and heavy chains of mouse, achieving 97.5-100% coverage and 97.2-99.5% accuracy, without assisting databases. Moreover, DeepNovo is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution to the de novo sequencing problem. Not only does our study extend the deep learning revolution to a new field, but it also shows an innovative approach in solving optimization problems by using deep learning and dynamic programming.deep learning | MS | de novo sequencing P roteomics research focuses on large-scale studies to characterize the proteome, the entire set of proteins, in a living organism (1-5). In proteomics, de novo peptide sequencing from tandem MS data plays the key role in the characterization of novel protein sequences. This field has been actively studied over the past 20 y, and many de novo sequencing tools have been proposed, such as PepNovo, PEAKS, NovoHMM, MSNovo, pNovo, UniNovo, and Novor among others (6-19). The recent "gold rush" into mAbs has undoubtedly elevated the application of de novo sequencing to a new horizon (20-23). However, computational challenges still remain, because MS/MS spectra contain much noise and ambiguity that require rigorous global optimization with various forms of dynamic programming that have been developed over the past decade (8-10, 12, 13, 15-19, 24).In this study, we introduce neural networks and deep learning to de novo peptide sequencing and achieve major breakthroughs on this well-studied problem. Deep learning has recently brought about a revolution in many research fields (25), repeatedly breaking state of the art records in image processing (26, 27), speech recognition (28), and natural language processing (29). It now forms the core of the artificial intelligence platforms of several technology giants, such as Google, Facebook, and Microsoft, as well as many startups in the industry. Deep learning has also made its way into biological sciences (30) [for instance, in the field of genomics, where deep neural network models have been developed for predicting the effects of noncoding single-nucleotide ...

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