More Like this

1. Modulating Responses of Toehold Switches by an Inhibitory Hairpin

   https://doi.org/10.1021/acssynbio.8b00488  

   Kim, Soo-Jung; Leong, Matthew; Amrofell, Matthew B.; Lee, Young Je; Moon, Tae Seok (January 2019, ACS Synthetic Biology)
2. Strain Structure and Dynamics Revealed by Targeted Deep Sequencing of the Honey Bee Gut Microbiome

   https://doi.org/10.1128/mSphere.00694-20  

   Bobay, Louis-Marie; Wissel, Emily F.; Raymann, Kasie (August 2020, mSphere)

   Campbell, Barbara J. (Ed.)

   ABSTRACT Host-associated microbiomes can be critical for the health and proper development of animals and plants. The answers to many fundamental questions regarding the modes of acquisition and microevolution of microbiome communities remain to be established. Deciphering strain-level dynamics is essential to fully understand how microbial communities evolve, but the forces shaping the strain-level dynamics of microbial communities remain largely unexplored, mostly because of methodological issues and cost. Here, we used targeted strain-level deep sequencing to uncover the strain dynamics within a host-associated microbial community using the honey bee gut microbiome as a model system. Our results revealed that amplicon sequencing of conserved protein-coding gene regions using species-specific primers is a cost-effective and accurate method for exploring strain-level diversity. In fact, using this method we were able to confirm strain-level results that have been obtained from whole-genome shotgun sequencing of the honey bee gut microbiome but with a much higher resolution. Importantly, our deep sequencing approach allowed us to explore the impact of low-frequency strains (i.e., cryptic strains) on microbiome dynamics. Results show that cryptic strain diversity is not responsible for the observed variations in microbiome composition across bees. Altogether, the findings revealed new fundamental insights regarding strain dynamics of host-associated microbiomes. IMPORTANCE The factors driving fine-scale composition and dynamics of gut microbial communities are poorly understood. In this study, we used metagenomic amplicon deep sequencing to decipher the strain dynamics of two key members of the honey bee gut microbiome. Using this high-throughput and cost-effective approach, we were able to confirm results from previous large-scale whole-genome shotgun (WGS) metagenomic sequencing studies while also gaining additional insights into the community dynamics of two core members of the honey bee gut microbiome. Moreover, we were able to show that cryptic strains are not responsible for the observed variations in microbiome composition across bees. 

   more » « less
3. Deep Learning Methods for De Novo Peptide Sequencing

   https://doi.org/10.1002/mas.21919  

   Bittremieux, Wout; Ananth, Varun; Fondrie, William_E; Melendez, Carlo; Pominova, Marina; Sanders, Justin; Wen, Bo; Yilmaz, Melih; Noble, William_S (November 2024, Mass Spectrometry Reviews)

   ABSTRACT Protein tandem mass spectrometry data are most often interpreted by matching observed mass spectra to a protein database derived from the reference genome of the sample being analyzed. In many application domains, however, a relevant protein database is unavailable or incomplete, and in such settings de novo sequencing is required. Since the introduction of the DeepNovo algorithm in 2017, the field of de novo sequencing has been dominated by deep learning methods, which use large amounts of labeled mass spectrometry data to train multi‐layer neural networks to translate from observed mass spectra to corresponding peptide sequences. Here, we describe these deep learning methods, outline procedures for evaluating their performance, and discuss the challenges in the field, both in terms of methods development and evaluation protocols. 

   more » « less
4. Overlearning hyperstabilizes a skill by rapidly making neurochemical processing inhibitory-dominant

   https://doi.org/10.1038/nn.4490  

   Shibata, Kazuhisa; Sasaki, Yuka; Bang, Ji Won; Walsh, Edward G; Machizawa, Maro G; Tamaki, Masako; Chang, Li-Hung; Watanabe, Takeo (March 2017, Nature Neuroscience)
5. Insect and plant-derived miRNAs in greenbug (Schizaphis graminum) and yellow sugarcane aphid (Sipha flava) revealed by deep sequencing

   https://doi.org/10.1016/j.gene.2016.11.014  

   Wang, Haichuan; Zhang, Chi; Dou, Yongchao; Yu, Bin; Liu, Yunfeng; Heng-Moss, Tiffany M.; Lu, Guoqing; Wachholtz, Michael; Bradshaw, Jeffery D.; Twigg, Paul; et al (January 2017, Gene)