Nop9 is an RNA binding protein present in pre-40S ribosomes and required for 18S rRNA synthesis in yeast

Proteomic analyses in yeast have identified a large number of proteins that are associated with preribosomal particles. However, the product of the yeast ORF YJL010C, herein designated as Nop9, failed to be identified in any previous physical or genetic analysis of preribosomes. Here we report that Nop9 is a nucleolar protein, which is associated with 90S and 40S preribosomes. In cells depleted of Nop9p, early cleavages of the 35S pre-rRNA are inhibited, resulting in the nucleolar retention of accumulated precursors and a failure to synthesize 18S rRNA. Nop9 contains multiple pumilio-like putative RNA binding repeats and displays robust in vitro RNA binding activity. The identification of Nop9p as a novel, essential factor in the nuclear maturation of 90S and pre-40S ribosomal subunits shows that the complement of ribosome synthesis factors remains incomplete

Departmental seminar series and journal club with enhanced learning outcomes

Listening to scientific presentations and reading scientific literature are core activities of any scientist, and frequent components of students' curricula. When employing these activities in teaching, finding the right balance between student instruction and autonomous learning is important for best learning outcomes and teachers’ workload. We here present our course design for a coordinated lecture series and journal club, that finds this balance by leveraging modern learning concepts in a digital environment. Participating students were tasked to read a landmark scientific paper every week ahead of a lecture by a scientist with practical experience on the topic of that paper, often an author of that week’s paper. Students then had to hand in written answers to three questions probing their understanding of the topic and the paper. In a subsequent seminar, activating questions were discussed by the students in break-out rooms and then answered by randomly chosen students in class, followed by a broad discussion that included the homework questions. Students gave weekly feedback on their learning progress and experience, and the course was then dynamically adapted accordingly. This yielded a course with largely increased course capacity, reduced teachers’ workload, and substantially enhanced learning outcomes, qualitatively and quantitatively compared to previous implementations of the course

From student to expert in a week

It can be challenging to effectively impart higher education content to students. We experienced such difficulty in a lecture series with invited senior scientists presenting their area of Biotech research. Instead of a vivid exchange with the expert, we observed limited and restrained student contributions. In qualitative interviews with these students we learned that they perceive their knowledge disparity as too big and the fear of being embarrassed by asking “stupid” questions obstructed their participation. This let us to radically rethink the course design resulting in our own interpretation of flipped classroom, peer learning and student empowerment. We designed an engineering course that focuses on providing master students with the best possible environment to gain theoretical knowledge in a new field within a limited time period (currently: six weeks - six topics) aiming to empower them in these topics by acquiring new knowledge on their own. Based on seed questions and tag words, students conduct background research and create a team presentation for an invited field expert, thereby getting prepared for a subsequent indepth discussion with the expert. The current layout is the product of an iterative process over the course of five years, and several rounds of fine-tuning within each year, based on extensive student and instructor feedback. Students particularly appreciate the positive in-course atmosphere with a focus on growth-mindset, the strong experience in teamwork, being taken seriously, and making contact with field experts and frontiers of current knowledge

Peptide Retention in Hydrophilic Strong Anion Exchange Chromatography Is Driven by Charged and Aromatic Residues

Hydrophilic strong anion exchange chromatography (hSAX) is becoming a popular method for the prefractionation of proteomic samples. However, the use and further development of this approach is affected by the limited understanding of its retention mechanism and the absence of elution time prediction. Using a set of 59 297 confidentially identified peptides, we performed an explorative analysis and built a predictive deep learning model. As expected, charged residues are the major contributors to the retention time through electrostatic interactions. Aspartic acid and glutamic acid have a strong retaining effect and lysine and arginine have a strong repulsion effect. In addition, we also find the involvement of aromatic amino acids. This suggests a substantial contribution of cation−π interactions to the retention mechanism. The deep learning approach was validated using 5-fold cross-validation (CV) yielding a mean prediction accuracy of 70% during CV and 68% on a hold-out validation set. The results of this study emphasize that not only electrostatic interactions but rather diverse types of interactions must be integrated to build a reliable hSAX retention time predictor

Quirks of Error Estimation in Cross-Linking/Mass Spectrometry

Cross-linking/mass spectrometry is an increasingly popular approach to obtain structural information on proteins and their complexes in solution. However, methods for error assessment are under current development. We note that false-discovery rates can be estimated at different points during data analysis, and are most relevant for residue or protein pairs. Missing this point led in our example analysis to an actual 8.4% error when 5% error was targeted. In addition, prefiltering of peptide-spectrum matches and of identified peptide pairs substantially improved results. In our example, this prefiltering increased the number of residue pairs (5% FDR) by 33% (<i>n</i> = 108 to <i>n</i> = 144). This number improvement did not come at the expense of reduced accuracy as the added data agreed with an available crystal structure. We provide an open-source tool, xiFDR (https://github.com/rappsilberlab/xiFDR), that implements our observations for routine application. Data are available via ProteomeXchange with identifier PXD004749

A Primer on Data Analytics in Functional Genomics:How to Move from Data to Insight?

High-throughput technologies are now widely used in the life sciences field and are producing ever-increasing amounts and diversity of data. While many laboratories and even undergraduate students generate high-throughput data, analyzing these results requires a skill set that is traditionally reserved for bioinformaticians. Learning to program using languages such as R and Python and making sense of the vast amounts of available omics data have become easier, thanks to the multitude of available resources. This can empower bench-side researchers to perform more complex computational analyses. Tools such as KNIME or Galaxy (together with a growing number of tutorials and courses) have been crucial in providing simple user interfaces to conduct complex analyses under the hood, making the ‘big data’ revolution accessible to biologists. High-throughput methodologies and machine learning have been central in developing systems-level perspectives in molecular biology. Unfortunately, performing such integrative analyses has traditionally been reserved for bioinformaticians. This is now changing with the appearance of resources to help bench-side biologists become skilled at computational data analysis and handling large omics data sets. Here, we show an entry route into the field of omics data analytics. We provide information about easily accessible data sources and suggest some first steps for aspiring computational data analysts. Moreover, we highlight how machine learning is transforming the field and how it can help make sense of biological data. Finally, we suggest good starting points for self-learning and hope to convince readers that computational data analysis and programming are not intimidating