With their input, we compiled a list of some of resources and tools in this field.

We can gladly include additional resources. Please comment on this page. You can also tweet us: @naturemethods or @metricausa

Some resources and tools related to noncoding RNAs:

Sources: H. Chang, Stanford University School of Medicine; Rory Johnson, University of Bern, E. Marshall, BC Cancer Agency; M. Turner, Babraham Institute; U. Ohler, Max Delbrück Center for Molecular Medicine; I. Rigoutsos, Philadelphia University + Thomas Jefferson University; Nature Research.

A conversation about neuroscience
presented by Nature Methods, Nature Neuroscience & Nature Communications

In advance of the 2017 annual meeting of the Society for Neuroscience, Jean Mary Zarate, an editor at Nature Neuroscience and Sachin Ranade , an editor at Nature Communications (photo, upper left) had the opportunity to speak with Eric Nestler, President of the Society for Neuroscience and researcher at the Icahn School of Medicine at Mt. Sinai and Robert Greene, a scientist at the University of Texas Southwestern Medical Center (photo, upper right).

Watch the video here

Though much of the excitement about cryo-EM is quite recent, the Nobel Prize is a good reminder to us all that the essential foundations of this technology were laid decades ago. We celebrated such developments, both old and new, in our 2015 Method of the Year issue featuring cryo-EM.

To commemorate this well-deserved Nobel Prize, Nature Research presents an editorially curated collection of papers published in our pages – including methods and protocols, biological results generated using cryo-EM technology, and reviews, news and comment. Check it out!

Earlier this year, the EuXFEL’s first laser beam
reached the ‘hutch’.{credit}Jessica Mancuso{/credit}

As of September 1, the European X-ray free-electron laser (EuXFEL) is ready for the research community’s experiments; the user page is here.

In the September issue of Nature Methods, we present some of the experimental ideas researchers are exploring in that facility.

Other XFELs are operational or in the works: FERMI facility, Linac Coherent Light Source at Stanford (LCLS), Pohang Accelerator Laboratory (PAL) X-ray Free-Electron Laser, SPring-8 Angstrom Compact Free Electron, Laser (SACLA), Swiss Free-Electron Laser (SwissFEL).

One day, there might even be a XFEL that fits on a table-top (see below). The day is already here when scientists need to analyze mountains of XFEL-data. The EuXFEL will likely make those mountains grow in height. There are tools for that and likely more tools to come (see far below for a list of some tools).

Tabletop XFEL

To complement the large XFEL facilities, a number of research groups are developing benchtop XFELs^9,10. Such projects involve miniaturization of all aspects of the technology, including the accelerator. Some groups explore ways to pass a laser through plasma to produce bright, high-energy, short-pulsed beams. Separately, some researchers use a terahertz generator, which can provide sufficiently high pulse energies, says Franz Kärtner, a physicist at the University of Hamburg who also holds an appointment at MIT. His team, along with Petra Fromme of Arizona State University, is developing such a tabletop XFEL instrument.

The scientists would like to use the instrument for coherent diffractive imaging and spectroscopy experiments on photosystem II, a protein complex involved in photosynthesis. Their compact XFEL approach, which will use a terahertz generator, lasers and nonlinear optics, is calculated to achieve photon energies between 10 and 12 keV, hard X-rays that can be harnessed for imaging at atomic resolution, says Kärtner.

Although this compact XFEL will generate fewer photons per shot than a large-scale FEL—10⁶ to 10⁹ photons per shot as opposed to 10¹² photons or more—the machine will be able to produce very short pulses, on the order of 0.5 femtoseconds. That is 10–100 times shorter than current FEL pulses. And if that comes to be, says Kärtner, the peak power of the instrument may be almost on par with that of an XFEL.

A terahertz accelerator module for a table-top XFEL in the making
{credit}DESY/Heiner Müller-Elsner{/credit}

In the instrument’s terahertz-driven accelerator there will be acceleration gradients between 500 MV/m to 1GV/m. It’s this high frequency that helps to compress electron bunches over short distances and that will let the developers to use compact electron guns. In this fashion, they will be able to shoot a coherent electron beam directly from a gun and emit an X-ray beam much like a FEL, says Kärtner.

Tabletop XFELs fill an important experimental gap between Röntgen’s X-ray tube and the large-scale FELs. “There is nothing in between,” says Kärtner. That’s akin to a situation in optical science in which researchers need to choose between a light bulb and a large-scale optical laser such as the one at the National Ignition Facility at Lawrence Livermore National Laboratory. If the developers of the compact XFEL succeed at packing enough photons into each shot, the instrument will have potential applications in many fields, he says, including enhanced characterization of materials or higher-resolution medical and structural biology imaging.

1.  Kneip, et al. Nat. Phys. 6, 980–983 (2010).
2.  Kärtner, X. et al. Nucl. Instrum. Methods Phys. Res. A 829, 24–29 (2016)

Data mountains

XFEL-based experiments produce mountains of data. At EuXFEL, there are two two-dimensional pixel detectors, which will each deliver 10-40 gigabytes of data every second of an experiment.

Experimental data will be housed in the facility’s online systems and then moved to offline disk-based systems also at the facility where researchers can access and analyze it, says Filipe Maia, a biophysicist at the Uppsala University.

The data torrent makes for “a daunting problem,” says Maia, “and currently there’s clearly a lack of user friendly tools.” This issue is a general trend and not unique to XFEL-based research, but researchers are getting better at handling datasets, which happens also as they familiarize themselves with the increasingly available tools. After publication, he hopes the XFEL-data will be transmitted to an online repository to share it with the community. One such resource is the Coherent X-ray Imaging Data Bank (CXIDB), which he built.

Here are some tools for analyzing and managing XFEL data                             

Sources: Henry Chapman, DESY; Janos Hajdu, Filipe Maia, Uppsala University; Sébastien Boutet, LCLS

LCLS: Stanford Linac Coherent Light Source; Linac Coherent Light Source, SLAC National Accelerator Laboratory (formerly named Stanford Linear Accelerator Center)
SACLA: Spring-8 Angstrom Compact Free Electron Laser
SFX: Serial Femtosecond Crystallography
XFEL: X-ray free-electron laser

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As additional reading, we suggest a 2013 editorial on phototoxicity from our pages.

We welcome any feedback!

From June 14-17, scientists convened in Boston for the annual meeting of the International Society for Stem Cell Research (ISSCR). Here is the browsable program.

In advance of the meeting, we had the opportunity to chat with Sally Temple, president of ISSCR, co-founder and scientific director of the Neural Stem Cell Institute and Lorenz Studer, founder and director of the Center for Stem Cell Biology at Memorial Sloan Kettering Cancer Center to hear about some of the sessions but also to learn about some larger trends shaping the stem cell biology field.

Here is a video (19 minutes).

And here is a podcast of this conversation, both moderated by Tal Nawy, a senior editor at Nature Methods.

Glycoscience is sweet science{credit}PhotoDisc/ Getty Images{/credit}

As glycoscience advances, labs will increasingly want to ask questions about glycosylation sites on a protein or the structure of a sugar, says Raja Mazumder, a bioinformatician at George Washington University. They might ask for example: are there glycosyltransferases that are expressed in liver but not in the heart, or, which ones are overexpressed by a factor of three in more than two cancers. Such questions require infrastructure building, he says, because right now there is no mechanism to allow such queries. But he and others are building such capabilities. Mazumder along with William York at the University of Georgia are starting to build a glycoscience informatics portal.

Mazumder wants to leverage existing ontologies in the developer community in order to build systems that can be queried on a large-scale. For example, Mazumder is working with Cathy Wu at Georgetown University, who is developing the Protein Ontology. Such ontologies are collected, for example, by the non-profit OBO Foundry. To allow flexible querying, the computational resources will draw on different ontologies; ones that relate to glycans, genes, proteins, tissues, diseases and more.

Ontologies are part the team’s effort to build application program interfaces (APIs) that expose the data in a given database to incoming queries. Given how complex sugars are, the informatics framework has to be well-organized for both human and machine-based querying, says Mazumder.

When using the resource, a researcher will receive results that also document the search process itself such as the version of the queried database. “You need to be able to tell where you got that information from,” says Mazumder. Tracking data provenance matters especially in an age when databases continuously integrate information emerging in the literature.

For the Food and Drug Administration, Mazumder is developing computational standards for high-throughput sequencing, which he wants to also apply to glycoscience. His ‘biocompute object’ captures the given computational workflow a lab might have used to generate results: the software used, the databases queried and their version, and identifiers of data inputs and outputs. These biocompute objects are intended to help regulatory scientists interpret submitted work. It can also help scientists generally see if, for example, the version of software they used worked as it should, says Mazumder.

Too often labs use computational tools without benchmarking them, says Mazumder. “It would be unthinkable for a wet-lab scientist to not have a positive and negative control,” he says.  In informatics, developers benchmark their software but users often do not have these habits. “They don’t even know: if I don’t find anything, is it because my software did not run well or not?”

As labs move to big data analysis in genomics and also, eventually, in glycoscience, this aspect is ever more important, says Mazumder. In his view, biocompute objects will help glycobiology researchers communicate with one another about their results, such as where on a protein they found a sugar with a given structure. More generally, it will help glycoscientists to have a better way to connect the available sugar resources as they pursue their questions of interest.

Here are some resources that glycoscientists can tap into:                             

Sources: NIH, R. Mazumder, George Washington University; New England Biolabs, Thermo Fisher Scientific, Nature Research

Comments on this subject are welcome!